Compositions, methods and systems for protein corona analysis and uses thereof

ABSTRACT

Compositions, methods, and systems for analyzing the protein corona are described herein, as well as its application in the discovery of advanced diagnostic tools as well as therapeutic targets.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of International PatentApplication No. PCT/US2019/000061, filed Nov. 7, 2019, which claims thebenefit of U.S. Provisional Patent Application No. 62/756,960, filedNov. 7, 2018, U.S. Patent Application No. 62/824,278, filed Mar. 26,2019 and U.S. Patent Application No. 62/874,862, filed Jul. 16, 2019,each of which is incorporated herein by reference in its entirety.

BACKGROUND

Broad scale implementation of proteomic information in science andmedicine has lagged behind genomics in large part because ofcomplexities inherent in protein molecules themselves, necessitatingcomplex workflows that limit the scalability of such analyses. Disclosedherein are compositions and methods for rapid processing of proteomicdata and identification of key biomarkers associated with disease.

SUMMARY

The present invention provides a panel of nanoparticles for thedetection of a wide range of diseases and disorders and determination ofdisease states in a subject.

Creating and characterizing protein coronas has also been performed inthe field; however, most of the experimentation has been done onnon-magnetic particles, such as liposomes and polymeric nanoparticles,or other particle types that may be used for targeted drug delivery.

The problem with the current (typically academic) assays for proteincorona formation and characterization is that they are not amenable tohigh-throughput and/or automated formats, as the particles used in theassay need to be isolated for corona collection. The advantage of SPMNPsused for protein coronas is their quick magnetic response that can beeasily separated from the suspension mixture by applying an externalmagnetic field. This type of magnetic particle is a good platform forfurther chemical modification with different functional groups which canhelp fine tune the interaction between particles and plasma proteins.

In some aspects, the present disclosure provides a method of identifyingproteins in a sample, the method comprising: incubating a particle panelwith the sample to form a plurality of distinct biomolecule coronascorresponding to distinct particle types of the particle panel;

magnetically isolating the particle panel from unbound protein in thesample to enrich proteins in the plurality of distinct biomoleculecoronas; and assaying the plurality of distinct biomolecule coronas toidentify the enriched proteins.

In some aspects, the assaying is capable of identifying from 1 to 20,000protein groups. In further aspects, the assaying is capable ofidentifying from 1000 to 10,000 protein groups. In further aspects, theassaying is capable of identifying from 1,000 to 5,000 protein groups.In still further aspects, the assaying is capable of identifying from1,200 to 2,200 protein groups. In some aspects, the protein groupcomprises a peptide sequence having a minimum length of 7 amino acidresidues. In some further, the assaying is capable of identifying from1,000 to 10,000 proteins. In some still further, the assaying is capableof identifying from 1,800 to 5,000 proteins.

In some embodiments, the sample comprises a plurality of samples. Insome embodiments, the plurality of samples comprises at least two ormore spatially isolated samples. In further embodiments, the incubatingcomprises contacting the at least two or more spatially isolated sampleswith the particle panel at the same time. In further embodiments, themagnetically isolating comprises magnetically isolating the particlepanel from unbound protein in the at least two or more spatiallyisolated samples of the plurality of samples at the same time. Infurther embodiments, the assaying comprises assaying the plurality ofdistinct biomolecule coronas to identify proteins in the at least two ormore spatially isolated samples at the same time.

In some embodiments, the method further comprises repeating the methodsdescribed herein, wherein, when repeated, the incubating, isolating, andassaying yields a percent quantile normalized coefficient (QNCV) ofvariation of 20% or less, as determined by comparing a peptide massspectrometry feature from at least three full-assay replicates for eachparticle type in the particle panel. In some embodiments, when repeated,the incubating, isolating, and assaying yields a percent quantilenormalized coefficient (QNCV) of variation of 10% or less, as determinedby comparing a peptide mass spectrometry feature from at least threefull-assay replicates for each particle type in the particle panel. Insome embodiments, the assaying is capable of identifying proteins over adynamic range of at least 7, at least 8, at least 9, or at least 10.

In some embodiments, the method further comprises washing the particlepanel at least one time or at least two times after magneticallyisolating the particle panel from the unbound protein. In someembodiments, after the assaying the method further comprises lysing theproteins in the plurality of distinct biomolecule coronas.

In some embodiments, the method further comprises digesting the proteinsin the plurality of distinct biomolecule coronas to generate digestedpeptides.

In some embodiments, the method further comprises purifying the digestedpeptides.

In some embodiments, the assaying comprises using mass spectrometry toidentify proteins in the sample. In some embodiments, the assaying isperformed in about 2 to about 4 hours. In some embodiments, the methodis performed in about 1 to about 20 hours. In some embodiments, themethod is performed in about 2 to about 10 hours. In some embodiments,the method is performed in about 4 to about 6 hours. In someembodiments, the isolating takes no more than about 30 minutes, no morethan about 15 minutes, no more than about 10 minutes, no more than about5 minutes, or no more than about 2 minutes. In some embodiments, theplurality of samples comprises at least 10 spatially isolated samples,at least 50 spatially isolated samples, at least 100 spatially isolatedsamples, at least 150 spatially isolated samples, at least 200 spatiallyisolated samples, at least 250 spatially isolated samples, or at least300 spatially isolated samples. In further embodiments, the plurality ofsamples comprises at least 96 samples.

In some embodiments, the particle panel comprises at least 2 distinctparticle types, at least 3 distinct particle types, at least 4 distinctparticle types, at least 5 distinct particle types, at least 6 distinctparticle types, at least 7 distinct particle types, at least 8 distinctparticle types, at least 9 distinct particle types, at least 10 distinctparticle types, at least 11 distinct particle types, at least 12distinct particle types, at least 13 distinct particle types, at least14 distinct particle types, at least 15 distinct particle types, atleast 20 distinct particle types, at least 25 particle types, or atleast 30 distinct particle types. In further embodiments, the particlepanel comprises at least 10 distinct particle types. In furtherembodiments, the at least two spatially isolated samples differ by atleast one physicochemical property.

In some embodiments, the particle panel comprises a first distinctparticle type and a second distinct particle type, wherein the firstdistinct particle type and the second distinct particle type share atleast one physicochemical property and differ by at least onephysicochemical property, such that the first distinct particle type andthe second distinct particle type are different. In some embodiments,the particle panel comprises a first distinct particle type and a seconddistinct particle type, wherein the first distinct particle type and thesecond distinct particle type share at least two physicochemicalproperties and differ by at least two physicochemical properties, suchthat the first distinct particle type and the second distinct particletype are different. In some embodiments, the particle panel comprises afirst distinct particle type and a second distinct particle type,wherein the first distinct particle type and the second distinctparticle type share at least one physicochemical property and differ byat least two physicochemical properties, such that the first distinctparticle type and the second distinct particle type are different.

In some embodiments, the particle panel comprises a first distinctparticle type and a second distinct particle type, wherein the firstdistinct particle type and the second distinct particle type share atleast two physicochemical properties and differ by at least onephysicochemical property, such that the first distinct particle type andthe second distinct particle type are different. In further embodiments,the physicochemical property comprises size, charge, core material,shell material, porosity, or surface hydrophobicity. In furtherembodiments, size is diameter or radius, as measured by dynamic lightscattering, SEM, TEM, or any combination thereof.

In some embodiments, the particle panel comprises a first distinctparticle type and a second distinct particle type, wherein the firstdistinct particle type and the second distinct particle type comprise acarboxylate material, wherein the first distinct particle is amicroparticle, and wherein the second distinct particle type is ananoparticle. In some embodiments, the particle panel comprises a firstdistinct particle type and a second distinct particle type, wherein thefirst distinct particle type and the second distinct particle typecomprise a surface charge of from 0 mV and −50 mV, wherein the firstdistinct particle type has a diameter of less than 200 nm, and whereinthe second distinct particle type has a diameter of greater than 200 nm.

In some embodiments, the particle panel comprises a first distinctparticle type and a second distinct particle type, wherein the firstdistinct particle type and the second distinct particle type comprise adiameter of 100 to 400 nm, wherein the first distinct particle type hasa positive surface change, and wherein the second distinct particle typehas a neutral surface charge. In some embodiments, the particle panelcomprises a first distinct particle type and a second distinct particletype, wherein the first distinct particle type and the second distinctparticle type are nanoparticles, wherein the first distinct particletype has a surface change less than −20 mV and the second distinctparticle type has a surface charge greater than −20 mV.

In some embodiments, the particle panel comprises a first distinctparticle type and a second distinct particle type, wherein the firstdistinct particle type and the second distinct particle type aremicroparticles, wherein the first distinct particle type has a negativesurface charge, and wherein the second distinct particle type has apositive surface charge. In some embodiments, the particle panelcomprises a subset of negatively charged nanoparticles, wherein eachparticle of the subset differ by at least one surface chemical group. Insome embodiments, the particle panel comprises a first distinct particletype, a second particle, and a third distinct particle type, wherein thefirst distinct particle type, the second distinct particle type, and thethird distinct particle type comprise iron oxide cores, polymer shells,and are less than about 500 nm in diameter and wherein the firstdistinct particle type comprises a negative charge, the second distinctparticle type comprises a positive charge, and the third distinctparticle type comprises a neutral charge, wherein the diameter is a meandiameter as measured by dynamic light scattering. In furtherembodiments, the first distinct particle type comprises a silicacoating, the second distinct particle type comprises apoly(N-(3-(dimethylamino)propyl) methacrylamide) (PDMAPMA), and thethird distinct particle type comprises a poly(oligo(ethylene glycol)methyl ether methacrylate) (POEGMA) coating.

In some embodiments, at least one distinct particle type of the particlepanel is a nanoparticle. In some embodiments, at least one distinctparticle type of the particle panel is a microparticle. In someembodiments, at least one distinct particle type of the particle panelis a superparamagnetic iron oxide particle. In some embodiments, eachparticle of the particle panel comprise an iron oxide material. In someembodiments, at least one distinct particle type of the particle panelhas an iron oxide core. In some embodiments, at least one distinctparticle type of the particle panel has iron oxide crystals embedded ina polystyrene core. In some embodiments, each distinct particle type ofthe particle panel is a superparamagnetic iron oxide particle. In someembodiments, each distinct particle type of the particle panel comprisesan iron oxide core. In some embodiments, each one distinct particle typeof the particle panel has iron oxide crystals embedded in a polystyrenecore. In some embodiments, at least one distinct particle type ofparticle panel comprises a carboxylated polymer, an aminated polymer, azwitterionic polymer, or any combination thereof. In some embodiments,at least one particle type of the particle panel comprises an iron oxidecore with a silica shell coating. In some embodiments, at least oneparticle type of the particle panel comprises an iron oxide core with apoly(N-(3-(dimethylamino)propyl) methacrylamide) (PDMAPMA) coating. Insome embodiments, at least one particle type of the particle panelcomprises an iron oxide core with a poly(oligo(ethylene glycol) methylether methacrylate) (POEGMA) coating.

In some embodiments, at least one distinct particle type of the particlepanel comprises a negative surface charge. In some embodiments, at leastone distinct particle type of the particle panel comprises a positivesurface charge. In some embodiments, at least one distinct particle typeof the particle panel comprises a neutral surface charge. In someembodiments, the particle panel comprises one or more distinct particletypes selected from TABLE 10. In some embodiments, the particle panelcomprises two or more distinct particle types, three or more distinctparticle types, four or more distinct particle types, five or moredistinct particle types, six or more distinct particle types, seven ormore distinct particle types, eight or more distinct particle types,nine or more distinct particle types, or all ten distinct particle typesselected from TABLE 10. In some embodiments, the particle panelcomprises one or more distinct particle types selected from TABLE 12. Insome embodiments, the particle panel comprises two or more distinctparticle types, three or more distinct particle types, four or moredistinct particle types, five or more distinct particle types, six ormore distinct particle types, seven or more distinct particle types,eight or more distinct particle types, nine or more distinct particletypes, or all ten distinct particle types selected from TABLE 12.

In various aspects, the present disclosure provides a compositioncomprising three or more distinct magnetic particle types that differ bytwo or more physicochemical properties, wherein a subset of the three ormore distinct magnetic particle types share a physicochemical propertyof the two or more physicochemical properties and wherein such particletypes of the subset bind different proteins.

In some embodiments, the three or more distinct magnetic particle typesadsorb proteins from a biological sample over a dynamic range of atleast 7, at least 8, at least 9, or at least 10. In some embodiments,the three or more distinct magnetic particle types are capable ofadsorbing from 1 to 20,000 proteins groups from a biological sample. Insome aspects, the three or more distinct magnetic particle types arecapable of adsorbing from 1,000 to 10,000 protein groups from abiological sample. In further aspects, the three or more distinctmagnetic particle types are capable of adsorbing from 1,000 to 5,000protein groups from a biological sample. In further aspects, the threeor more distinct magnetic particle types are capable of adsorbing from1,200 to 2,200 protein groups from a biological sample. In still furtheraspects, wherein the protein group comprises a peptide sequence having aminimum length of 7 amino acid residues. In some aspects, the three ormore distinct magnetic particle types are capable of adsorbing from 1 to20,000 proteins from a biological sample. In further aspects, the threeor more distinct magnetic particle types are capable of adsorbing from1,000 to 10,000 proteins from a biological sample. In still furtheraspects, the three or more distinct magnetic particle types are capableof adsorbing from 1,800 to 5,000 proteins from a biological sample.

In some embodiments, the composition comprises at least 4 distinctmagnetic particle types, at least 5 distinct magnetic particle types, atleast 6 distinct magnetic particle types, at least 7 distinct magneticparticle types, at least 8 distinct magnetic particle types, at least 9distinct magnetic particle types, at least 10 distinct magnetic particletypes, at least 11 distinct magnetic particle types, at least 12distinct magnetic particle types, at least 13 distinct magnetic particletypes, at least 14 distinct magnetic particle types, at least 15distinct magnetic particle types, at least 20 distinct magnetic particletypes, or at least 30 distinct magnetic particle types. In someembodiments, the composition comprises at least 10 distinct magneticparticle types. In some embodiments, the composition comprises a firstdistinct particle type and a second distinct particle type, wherein thefirst distinct particle type and the second distinct particle type shareat least two physicochemical properties and differ by at least twophysicochemical properties, such that the first distinct particle typeand the second distinct particle type are different.

In some embodiments, the composition comprises a first distinct particletype and a second distinct particle type, wherein the first distinctparticle type and the second distinct particle type share at least twophysicochemical properties and differ by at least one physicochemicalproperty, such that the first distinct particle type and the seconddistinct particle type are different. In some embodiments, thephysicochemical property comprises size, charge, core material, shellmaterial, porosity, or surface hydrophobicity. In further embodiments,the size is diameter or radius, as measured by dynamic light scattering,SEM, TEM, or any combination thereof.

In some embodiments, the composition comprises a first distinct particletype and a second distinct particle type, wherein the first distinctparticle type and the second distinct particle type comprise acarboxylate material, wherein the first distinct particle is amicroparticle, and wherein the second distinct particle type is ananoparticle. In some embodiments, the particle panel comprises a firstdistinct particle type and a second distinct particle type, wherein thefirst distinct particle type and the second distinct particle typecomprise a surface charge of from 0 mV and −50 mV, wherein the firstdistinct particle type has a diameter of less than 200 nm, and whereinthe second distinct particle type has a diameter of greater than 200 nm.In some embodiments, the particle panel comprises a first distinctparticle type and a second distinct particle type, wherein the firstdistinct particle type and the second distinct particle type comprise adiameter of 100 to 400 nm, wherein the first distinct particle type hasa positive surface change, and wherein the second distinct particle typehas a neutral surface charge.

In some embodiments, the particle panel comprises a first distinctparticle type and a second distinct particle type, wherein the firstdistinct particle type and the second distinct particle type arenanoparticles, wherein the first distinct particle type has a surfacechange less than −20 mV and the second distinct particle type has asurface charge greater than −20 mV. In some embodiments, the particlepanel comprises a first distinct particle type and a second distinctparticle type, wherein the first distinct particle type and the seconddistinct particle type are microparticles, wherein the first distinctparticle type has a negative surface charge, and wherein the seconddistinct particle type has a positive surface charge. In someembodiments, the composition comprises a subset of negatively chargednanoparticles, wherein each particle type of the subset differ by atleast one surface chemical group. In some embodiments, the compositioncomprises a first distinct particle type, a second distinct particletype, and a third distinct particle type, wherein the first distinctparticle type, the second distinct particle type, and the third distinctparticle type comprise iron oxide cores, polymer shells, and are lessthan about 500 nm in diameter and wherein the first distinct particletype comprises a negative charge, the second distinct particle typecomprises a positive charge, and the third distinct particle typecomprises a neutral charge, wherein the diameter is a mean diameter asmeasured by dynamic light scattering. In further embodiments, the firstdistinct particle type comprises a silica coating, the second distinctparticle type comprises a poly(N-(3-(dimethylamino)propyl)methacrylamide) (PDMAPMA), and the third distinct particle typecomprises a poly(oligo(ethylene glycol) methyl ether methacrylate)(POEGMA) coating.

In some embodiments, the three or more distinct magnetic particle typescomprise a nanoparticle. In some embodiments, the three or more distinctmagnetic particle types comprises a microparticle. In some embodiments,at least one distinct particle type of the three or more distinctmagnetic particle types is a superparamagnetic iron oxide particle. Insome embodiments, at least one distinct particle type of the three ormore distinct magnetic particle types comprise an iron oxide material.In some embodiments, at least one distinct particle type of the three ormore distinct magnetic particle types has an iron oxide core. In someembodiments, at least one distinct particle type of the three or moredistinct magnetic particle types has iron oxide crystals embedded in apolystyrene core.

In some embodiments, each distinct particle type of the three or moredistinct magnetic particle types is a superparamagnetic iron oxideparticle. In some embodiments, each distinct particle type of the threeor more distinct magnetic particle types comprise an iron oxide core. Insome embodiments, each one distinct particle type of the three or moredistinct magnetic particle types has iron oxide crystals embedded in apolystyrene core. In some embodiments, at least one particle type of thethree or more distinct magnetic particle types comprises a polymercoating.

In some embodiments, the three or more distinct magnetic particle typescomprise a carboxylated polymer, an aminated polymer, a zwitterionicpolymer, or any combination thereof. In some embodiments, at least oneparticle type of the three or more distinct magnetic particle typescomprises an iron oxide core with a silica shell coating. In someembodiments, at least one particle type of the three or more distinctmagnetic particle types comprises an iron oxide core with apoly(N-(3-(dimethylamino)propyl) methacrylamide) (PDMAPMA) coating. Insome embodiments, at least one particle type of the three or moredistinct magnetic particle types comprises an iron oxide core with apoly(oligo(ethylene glycol) methyl ether methacrylate) (POEGMA) coating.

In some embodiments, at least one particle type of the three or moredistinct magnetic particle types comprises a negative surface charge. Insome embodiments, at least one particle type of the three or moredistinct magnetic particle types comprises a positive surface charge. Insome embodiments, at least one particle type of the three or moredistinct magnetic particle types comprises a neutral surface charge.

In some embodiments, the three or more distinct magnetic particle typescomprise one or more particle types of TABLE 10. In some embodiments,the particle panel comprises two or more distinct particle types, threeor more distinct particle types, four or more distinct particle types,five or more distinct particle types, six or more distinct particletypes, seven or more distinct particle types, eight or more distinctparticle types, nine or more distinct particle types, or all tendistinct particle types selected from TABLE 10. In some embodiments, thethree or more distinct magnetic particle types comprise one or moreparticle types of TABLE 12. In some embodiments, the particle panelcomprises two or more distinct particle types, three or more distinctparticle types, four or more distinct particle types, five or moredistinct particle types, six or more distinct particle types, seven ormore distinct particle types, eight or more distinct particle types,nine or more distinct particle types, or all ten distinct particle typesselected from TABLE 12.

In some aspects, the present disclosure provides a method of determiningthe biological state of a sample from a subject, comprising: exposing abiological sample to a panel comprising a plurality of nanoparticles,thereby generating a plurality of protein coronas; generating proteomicdata from the plurality of protein coronas; determining a proteinprofile of the plurality of protein coronas; and associating the proteinprofile to a biological state, wherein the panel comprises at least twodifferent nanoparticles.

In some embodiments, the panel comprises at least three differentnanoparticles. In some embodiments, the method associates the proteinprofile to the biological state with at least 90% accuracy. In someembodiments, the plurality of nanoparticles comprises at least one ironoxide nanoparticle.

In some aspects, the present disclosure provides, a method of selectinga panel for protein corona analysis, comprising selecting a plurality ofnanoparticles with at least three different physicochemical properties.

In some embodiments, the different physicochemical properties isselected from a group consisting of surface charge, surface chemistry,size, and morphology. In some embodiments, the different physicochemicalproperties comprise surface charge.

In various embodiments, the present disclosure provides a method ofidentifying proteins in a sample, the method comprising: incubating apanel comprising a plurality of particle types with the sample to form aplurality of protein corona; digesting the plurality of protein coronasto generate proteomic data; and identifying proteins in the sample byquantifying the proteomic data. In some embodiments, the sample is froma subject.

In some embodiments, the method further comprises determining a proteinprofile of the sample from the identifying step and associating theprotein profile with a biological state of the subject. In someembodiments, the method further comprises determining a biological stateof the sample from the subject by: generating proteomic data bydigesting the plurality of protein coronas; determining a proteinprofile of the plurality of protein coronas; and associating the proteinprofile with the biological state, wherein the panel comprises at leasttwo different nanoparticles. In some embodiments, the associating isperformed by a trained classifier.

In some embodiments, the panel comprises at least three differentparticle types, at least four different particle types, at least fivedifferent particle types, at least six different particle types, atleast seven different particle types, at least eight different particletypes, at least nine different particle types, at least 10 differentparticle types, at least 11 different particle types, at least 12different particle types, at least 13 different particle types, at least14 different particle types, at least 15 different particles, or atleast 20 different particle types. In some embodiments, the panelcomprises at least four different particle types. In some embodiments,at least one particle type of the panel comprises a physical featurethat is different from a second particle type of the panel. In someembodiments, the physical feature is size, polydispersity index, surfacecharge, or morphology. In some embodiments, the size of at least oneparticle type of the plurality of particle types in the panel is from 10nm to 500 nm.

In some embodiments, the polydispersity index of at least one particletype of the plurality of particle types in the panel is from 0.01 to0.25. In some embodiments, the morphology of at least one particle typeof the plurality of particle types comprises spherical, colloidal,square shaped, rods, wires, cones, pyramids, or oblong. In someembodiments, the surface charge of at least one particle type of theplurality of particle types comprises a positive surface charge. In someembodiments, the surface charge of at least one particle type of theplurality of particle types comprises a negative surface charge.

In some embodiments, the surface charge of at least one particle type ofthe plurality of particle types comprises a neutral surface charge. Insome embodiments, at least one particle type of the plurality ofparticle types comprises a chemical feature that is different from asecond particle type of the panel. In some embodiments, the chemicalfeature is a surface functional chemical group. In some embodiments, thefunctional chemical group is an amine or a carboxylate. In someembodiments, at least one particle type of the plurality of particletypes is made of a material comprising a polymer, a lipid, or a metal.

In further embodiments, the polymer comprises polyethylenes,polycarbonates, polyanhydrides, polyhydroxyacids, polypropylfumerates,polycaprolactones, polyamides, polyacetals, polyethers, polyesters,poly(orthoesters), polycyanoacrylates, polyvinyl alcohols,polyurethanes, polyphosphazenes, polyacrylates, polymethacrylates,polycyanoacrylates, polyureas, polystyrenes, or polyamines, apolyalkylene glycol (e.g., polyethylene glycol (PEG)), a polyester(e.g., poly(lactide-co-glycolide) (PLGA), polylactic acid, orpolycaprolactone), or a copolymer of two or more polymers.

In further embodiments, the lipid comprises dioleoylphosphatidylglycerol(DOPG), diacylphosphatidylcholine, diacylphosphatidylethanolamine,ceramide, sphingomyelin, cephalin, cholesterol, cerebrosides anddiacylglycerols, dioleoylphosphatidylcholine (DOPC),dimyristoylphosphatidylcholine (DMPC), and dioleoylphosphatidylserine(DOPS), phosphatidylglycerol, cardiolipin, diacylphosphatidylserine,diacylphosphatidic acid, N-dodecanoyl phosphatidylethanolamines,N-succinyl phosphatidylethanolamines,N-glutarylphosphatidylethanolamines, lysylphosphatidylglycerols,palmitoyloleyolphosphatidylglycerol (POPG), lecithin, lysolecithin,phosphatidylethanolamine, lysophosphatidylethanolamine,dioleoylphosphatidylethanolamine (DOPE), dipalmitoyl phosphatidylethanolamine (DPPE), dimyristoylphosphoethanolamine (DMPE),distearoyl-phosphatidyl-ethanolamine (DSPE),palmitoyloleoyl-phosphatidylethanolamine (POPE)palmitoyloleoylphosphatidylcholine (POPC), egg phosphatidylcholine(EPC), distearoylphosphatidylcholine (DSPC), dioleoylphosphatidylcholine(DOPC), dipalmitoylphosphatidylcholine (DPPC),dioleoylphosphatidylglycerol (DOPG), dipalmitoylphosphatidylglycerol(DPPG), palmitoyloleyolphosphatidylglycerol (POPG), 16-O-monomethyl PE,16-O-dimethyl PE, 18-1-trans PE,palmitoyloleoyl-phosphatidylethanolamine (POPE),1-stearoyl-2-oleoyl-phosphatidyethanolamine (SOPE), phosphatidylserine,phosphatidylinositol, sphingomyelin, cephalin, cardiolipin, phosphatidicacid, cerebrosides, dicetylphosphate, or cholesterol.

In some embodiments, the metal comprises gold, silver, copper, nickel,cobalt, palladium, platinum, iridium, osmium, rhodium, ruthenium,rhenium, vanadium, chromium, manganese, niobium, molybdenum, tungsten,tantalum, iron, or cadmium. In some embodiments, at least one particletype of the plurality of particle types is surface functionalized withpolyethylene glycol. In some embodiments, the method associates theprotein profile to the biological state with at least 70% accuracy, atleast 75% accuracy, at least 80% accuracy, at least 85% accuracy, atleast 90% accuracy, at least 92% accuracy, at least 95% accuracy, atleast 96% accuracy, at least 97% accuracy, at least 98% accuracy, atleast 99% accuracy, or 100% accuracy. In some embodiments, the methodassociates the protein profile to the biological state with at least 70%sensitivity, at least 75% sensitivity, at least 80% sensitivity, atleast 85% sensitivity, at least 90% sensitivity, at least 92%sensitivity, at least 95% sensitivity, at least 96% sensitivity, atleast 97% sensitivity, at least 98% sensitivity, at least 99%sensitivity, or 100% sensitivity.

In some embodiments, the method associates the protein profile to thebiological state with at least 70% specificity, at least 75%specificity, at least 80% specificity, at least 85% specificity, atleast 90% specificity, at least 92% specificity, at least 95%specificity, at least 96% specificity, at least 97% specificity, atleast 98% specificity, at least 99% specificity, or 100% specificity. Insome embodiments, wherein the method identifies at least 100 uniqueproteins, at least 200 unique proteins, at least 300 unique proteins, atleast 400 unique proteins, at least 500 unique proteins, at least 600unique proteins, at least 700 unique proteins, at least 800 uniqueproteins, at least 900 unique proteins, at least 1000 unique proteins,at least 1100 unique proteins, at least 1200 unique proteins, at least1300 unique proteins, at least 1400 unique proteins, at least 1500unique proteins, at least 1600 unique proteins, at least 1700 uniqueproteins, at least 1800 unique proteins, at least 1900 unique proteins,or at least 2000 unique proteins. In some embodiments, at least oneparticle type of the plurality of particle types comprises an iron oxidenanoparticle. In further embodiments, the sample is a biofluid. In stillfurther embodiments, the biofluid comprises plasma, serum, CSF, urine,tear, or saliva

In various embodiments, the present disclosure provides a method ofselecting a panel for protein corona analysis, comprising selecting aplurality of particle types with at least three differentphysicochemical properties. In some embodiments, the differentphysicochemical properties is selected from a group consisting ofsurface charge, surface chemistry, size, and morphology. In furtherembodiments, the different physicochemical properties comprises surfacecharge.

In various embodiments, the present disclosure provides a compositioncomprising a panel of particles, wherein the panel comprises a pluralityof particle types and wherein the plurality of particle types comprisesat least three different physicochemical properties. In someembodiments, the different physicochemical properties is selected from agroup consisting of surface charge, surface chemistry, size, andmorphology. In further embodiments, the different physicochemicalproperties comprises surface charge.

In various embodiments, the present disclosure provides a system ofcomprising any one of the above described panels.

In various embodiments, the present disclosure provides a systemcomprising a panel, wherein the panel comprises a plurality of particletypes. In some embodiments, the plurality of particle types comprises atleast three different physicochemical properties. In some embodiments,the panel comprises at least 3, at least 4, at least 5, at least 6, atleast 7, at least 8, at least 9, at least 10, at least 11, or at least12 different particle types. In some embodiments, the plurality ofparticle types are capable of adsorbing a plurality of proteins from asample to form a plurality of protein coronas. In some embodiments, theplurality of protein coronas are digested to determine a proteinprofile. In some embodiments, the protein profile is associated with abiological state using a trained classifier.

In some aspects, wherein the sample is a biological sample. In furtheraspects, the biological sample is plasma, serum, CSF, urine, tear, celllysates, tissue lysates, cell homogenates, tissue homogenates, nippleaspirates, fecal samples, synovial fluid and whole blood, or saliva. Insome aspects, wherein the sample is a non-biological sample. In furtheraspects, the non-biological sample is water, milk, solvents, or ahomogenized sample.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. The patent or application file contains at leastone drawing executed in color. Copies of this patent or patentapplication publication with color drawing(s) will be provided by theOffice upon request and payment of the necessary fee. A betterunderstanding of the features and advantages of the present inventionwill be obtained by reference to the following detailed description thatsets forth illustrative embodiments, in which the principles of theinvention are utilized, and the accompanying drawings of which:

FIG. 1 shows examples of surface chemistries for magnetic particles(MPs). In some cases, the magnetic particles may be magnetic corenanoparticles (MNP).

FIG. 2A shows the formation of protein corona on a particle. The profileof the protein corona depends on protein-particle, protein-protein, andprotein concentration factors. FIG. 2B shows the formation of proteincorona on three different particles. In some cases, the particles may benanoparticles. The properties of the particles result in differentprotein corona profiles.

FIG. 3 shows several examples of particle types and several ways theparticle surfaces can be functionalized. In some cases, the particlesmay be nanoparticles.

FIG. 4 shows the separation of superparamagnetic iron oxidenanoparticles (SPIONs) from the remaining solution. As illustrated inthe left photo, the SPIONs are dispersed in solution, seen as a dark,opaque solution in a glass vial, prior to or concurrent with applicationof a magnet to the side of the vial. Within 30 seconds of applying amagnet to the side of the vial, the SPIONs are separated from thesolution, as illustrated by accumulation of dark particles next to themagnet and an increase in solution transparency in the photo on theright. Upon shaking the separated solution shown in the right image, theparticles return to the dispersed state shown in the left image within 5seconds. The SPIONs have a fast response.

FIG. 5 provides an example of a process for generating proteomic dataand a process for panel selection.

FIG. 6 shows several examples of different attributes of particles andmethods of characterizing the particles.

FIG. 7 is an example of size distribution of nanoparticles ascharacterized by dynamic light scattering. FIG. 7 shows a dynamic lightscattering overlay of two particle types: SP-002 (phenol-formaldehydecoated particles) and SP-010 (carboxylate, PAA coated particles), bothof which have iron oxide cores. Dynamic light scattering can also beused to measure a size distribution of particles of larger sizes,including microparticles.

FIG. 8A and FIG. 8B show the characterization of nanoparticles withdifferent functionalization prior to corona formation. FIG. 8A shows thetransmission electron microscopy (TEM) of SP-002 (phenol-formaldehydecoated particles). FIG. 8B shows the TEM of SP-339 (polystyrene carboxylparticles). TEM can also be used to characterize particles of largersizes, including microparticles.

FIG. 9 shows the Fe 2p/3 spectrum for various nanoparticles includingSP-333 (carboxylate), SP-339 (polystyrene carboxylate), SP-356 (silicaamino), SP-374 (silica silanol), HX-20 (SP-003) (silica coated), HX-42(SP-006) (silica coated, amine), and HX-74 (SP-007) (PDMPAPMA coated(dimethylamine). The spectrum was obtained from XPS (x-ray photoelectronspectroscopy), which provides the chemical fingerprint of the particlesurfaces (measures % of various elements on the surface). XPS can alsobe used to measure spectra of particles of larger sizes, includingmicroparticles.

FIG. 10 shows an example of a process of the present disclosure forproteomic analysis. The process illustrated is optimized forhigh-throughput and automation that can be run in hours and acrossmultiple samples in parallel. The process includes particle-matrixassociation, particle wash (×3), formation of the protein corona,in-plate digestion, and mass spectrometry. Using the process, it maytake only 4 to 6 hours per batch of 96 samples. Typically, one particletype at a time is incubated with a sample.

FIG. 11 shows the protein counts (number of proteins identified fromcorona analysis) for panel sizes ranging from 1 particle type to 12particle types. Each particle in a panel may be unique in base material,surface functionalization, and/or physical property (e.g., size orshape). Single pooled plasma representing a pool of healthy subjects wasused. Counts are the numbers of unique proteins observed across thepanel of 12 particle types in about 2 hour mass spectrometry (MS) runs.1318 proteins were identified with a panel size of 12 particle types. Asused herein, a “feature” identified by mass spectrometry includes asignal at a specific combination of retention time and m/z(mass-to-charge ratio), where each feature has an associated intensity.Some features are further fragmented in a second mass spectrometryanalysis (MS2) for identification.

FIG. 12 represents gel electrophoresis analyses of multiple particletypes after incubation in plasma across multiple days. Each individualassay typically ran for about 1 hour, but triplicate measurements weremade on three different days, and by different operators, to demonstrateassay precision. From left to right, the gel shows a ladder, threeconsecutive columns of SP-339 (polystyrene carboxyl), three consecutivecolumns of SP-374 (silica silanol), a DNA ladder, and three consecutivecolumns of HX56 (SP-007) (silica amino).

FIG. 13 shows mass spectrometry analyses for protein identificationacross three separate assays for one particle type (SP-339, polystyrenecarboxyl). Across the three separate assays, 180 proteins were commonlyidentified.

FIG. 14A and FIG. 14B show the concentration responses for spikedproteins as compared to the controls. The spikes change withconcentration. Endogenous protein controls did not change withconcentration. FIG. 14A shows data from spike recovery experiments ofCRP. The protein was spiked at 4 levels: 2×, 5×, 10×, and 10×. HX-42(SP-006) (left) and HX-97 (right, same as SP-007) were used. FIG. 14Bshows the response slopes for spikes and controls. The slopes from theregression models fit to MS enzyme-linked immunosorbent assay (ELISA)data.

FIG. 15 shows a comparative study for evaluating particle types forpanel selection. 56 serum samples were obtained from 28 diseasedsubjects and 28 control subjects. The diseased subjects had confirmedStage IV non-small-cell lung carcinoma (NSCLC), comorbidities, andtreatments, such as diabetes, cardiovascular disease, hypertension, andetc. The control subjects were age- and gender-matched to the diseasedsubjects to reduce bias. The study was used to evaluate particle typesbetween groups with large differences, and shows they are age and gendermatched. Seven particles were used for the study, as shown in FIG. 16A.

FIG. 16A shows a comparison of samples (28 diseased vs. 28 controls)using a panel of seven particle types, including SP-339, HX74 (SP-007),SP-356, SP-333, HX20 (SP-003), SP-364, and HX42 (SP-006). 14,481filtered MS features were compared and 120 (0.8%) were different. FIG.16B shows the top hits from SP-339 of FIG. 16A. The detected differencesconfirm the ability of the coronas to differentiate between sample typesand ultimately build classifiers to define disease vs. healthy.

FIG. 17A shows a schematic of a process of the present applicationincluding collection of samples from healthy and cancer patients,isolation of plasma from the samples, incubation with uncoated liposomesto form protein coronas, and enrichment of select plasma proteins.Proteins in a sample were assayed using a particle-type panel withdistinct particle types to enrich proteins in distinct biomoleculecoronas formed on the distinct particle types. Protein corona formationis specific to the physicochemical properties of particles. FIG. 17Bshows corona analysis with an embodiment of the present disclosure, aProteograph, for a three-particle type panel. Plasma was collected from45 subjects (8 from each of 5 cancers, including glioblastoma, lungcancer, meningioma, myeloma, and pancreatic cancer, and 5 healthycontrols). The output of the corona analysis, Proteographs, were createdfor each particle in the 3-particle type panel. Random forest modelswere built in each of 1000 rounds of cross-validation. This providedstrong evidence for robust corona analysis signal. Initial exploratoryanalysis was done via principal component analysis (PCA) on the proteinsdetected from the combination of the three particles.

FIG. 18A and FIG. 18B show that early stage cancers can be separated upto 8 years before symptoms develop. The Golestan Cohort enrolled 50,000healthy subjects between 2004 and 2008. As shown in FIG. 18A, bankedplasma from enrollment was tested. 8 years after enrollment,approximately 1000 patients developed cancers. FIG. 18B shows theclassification of the banked plasma from FIG. 18A. Corona analysis ofbanked plasma from enrollment date accurately classified cancers for 15out of 15 subjects examined (5 patients each for 3 cancers).

FIG. 19 shows the robust classification of five cancers using athree-particle corona analysis with Proteograph with an overall accuracyof 95%. The data shows that adding particle type diversity increasesperformance. Three different liposomes with negative, neutral, andpositive net charge on the surfaces (at pH 7.4) were used.

FIG. 20 shows an example of scaling of particle biosensor production.The platform can be used across multiple assays and samples.

FIG. 21 illustrates an example of particle types of the presentdisclosure. The particle types may include nanoparticles (NPs) andmicroparticles.

FIG. 22A-B illustrate a schematic of the formation of particle proteincorona (FIG. 22A), and an embodiment of the present disclosure, theProteograph platform workflow, based on multi-particle type proteincorona approach and mass spectrometry for plasma proteome analysis (FIG.22B). FIG. 22A show three distinct particle types (depicted in thecenter of the figure, with the top, middle, and bottom spheresrepresenting the three distinct particle types), each different from theother by at least one physicochemical property, which leads to theformation of different protein corona compositions on the particlesurfaces. FIG. 22B shows the corona analysis workflow with Proteograph,which includes: (1) particle-plasma incubation and protein coronaformation; (2) particle protein corona purification by a magnet; (3)digestion of corona proteins; and (4) mass spectrometry analysis.

FIG. 23 illustrates characterization of the three superparamagnetic ironoxide nanoparticles (SPIONs) shown in the left-most first column, whichfrom top to bottom, are: silica-coated SPION (SP-003),poly(N-(3-(dimethylamino)propyl) methacrylamide) (PDMAPMA)-coated SPION(SP-007), and poly(oligo(ethylene glycol) methyl ether methacrylate)(POEGMA)-coated SPION (SP-011), by the following methods: scanningelectron microscopy (SEM, second columns of images), dynamic lightscattering (DLS, third column of graphs), transmission electronmicroscopy (TEM, fourth column of images), high-resolution transmissionelectron microscopy (HRTEM, fifth column), and X-ray photoelectronspectroscopy (XPS, sixth column), respectively. DLS shows threereplicates of each particle type. The HRTEM pictures were recorded atthe surface of individual SP-003, SP-007, and SP-011 particle types,respectively, and the arrow points to the region of amorphous SiO₂(topHRTEM image) coating and amorphous SiO₂/polymer coatings (middle andbottom HRTEM images) on the particle surface.

FIG. 24 shows the dynamic range for proteins observed on neat plasma vs.SP-003, SP-007, and SP-011 particles by comparison to a compileddatabase from Keshishian et al. (Mol Cell Proteomics. 2015 September;14(9):2375-93. doi: 10.1074/mcp.M114.046813. Epub 2015 Feb. 27.)(toppanel).

FIG. 25 shows a correlation of the maximum intensities of particlecorona proteins vs. plasma proteins to the published concentration ofthe same proteins

FIG. 26 shows the reproducibility of particle corona intensities foreach particle type (SP-003, SP-007, and SP-011) as demonstrated by threereplicates using the same plasma sample.

FIG. 27 shows changed features in a non-small cell lung cancer (NSCLC)pilot study using the SP-007 particles. Seven MS features wereidentified as statistically, significantly different between 28 subjectswith Stage IV NSCLC (with associated co-morbidities and treatmenteffects) and 28 age- and gender-matched, apparently healthy subjects.The table at bottom is a list of the seven proteins that weresignificantly different, including 5 known proteins and 2 unknownproteins. If a peptide-spectrum match was made for MS2 data associatedwith the feature, that peptide sequence (and charge) as well as thepotential parent protein are indicated; if an MS2 match was notassociated with the feature, both the peptide and the protein are markedas “Unknown”.

FIG. 28A shows a schematic for synthesis of SPION core.

FIG. 28B shows a schematic for synthesis of silica-coated SPION(SP-003).

FIG. 28C shows a schematic for synthesis of vinyl group functionalizedSPION.

FIG. 28D shows a schematic for synthesis ofpoly(N-(3-(dimethylamino)propyl) methacrylamide) (PDMAPMA)-coated SPION(SP-007).

FIG. 28E shows a schematic for synthesis of poly(oligo(ethylene glycol)methyl ether methacrylate) (POEGMA)-coated SPION (SP-011).

FIG. 29 shows the distribution of the presence-filtered, clusterquality-filtered, median-normalized MS feature intensities for the56-sample NSCLC comparative study. Each line represents the density ofthe log₂ feature intensity for either a diseased sample or a controlsample. Density is plotted from 0.00 to 0.15 on the y-axis, and log₂feature intensity is plotted from 15 to 35 on the x-axis. At the highestpeak located near a log₂ feature intensity of about 28, with densitiesranging from about 0.13 to about 0.17, the two highest traces correspondto control samples, while the lowest trace corresponds to a diseasedsample. The remaining control and diseased traces are distributedbetween the highest and lowest traces. At the two shoulder peaks,occurring at about 20 log₂ feature intensity and about 23 log₂ featureintensity, the highest two traces are control traces and the lowest twotraces are control traces at the 20 log₂ feature intensity peak, and thehighest traces is a diseased trace at the 23 log₂ feature intensity.

FIG. 30 shows the accuracy of measurements for C-reactive proteins (CRP)on the SP-007 nanoparticles in a spike-recovery experiment for fourdifferent peptides.

FIG. 31 shows the accuracy of measurement for peptide features ofAngiogenin in a spike-recovery experiment.

FIG. 32 shows the accuracy of measurement for peptide features of S10A8in a spike-recovery experiment.

FIG. 33 shows the accuracy of measurement for peptide features of S10A9in a spike-recovery experiment.

FIG. 34 shows the accuracy of measurement for peptide features ofC-reactive protein (CRP) in a spike-recovery experiment.

FIG. 35 shows the accuracy of measurement for protein features in aspike-recovery experiment.

FIG. 36 shows matching and coverage of a particle panel of the 10distinct particle types to a 5,304-plasma protein database of MSintensities. The ranked intensities for the database proteins are shownin the top panel (“Database”), the intensities for proteins from simpleplasma MS evaluation are shown in the second panel (“Plasma”) and theintensities for the optimal 10-particle type panel are shown in theremaining panels. The plasma protein intensities database is fromKeshishian et al. (2015). Multiplexed, Quantitative Workflow forSensitive Biomarker Discovery in Plasma Yields Novel Candidates forEarly Myocardial Injury. Molecular & Cellular Proteomics, 14(9),2375-2393.

FIG. 37 shows various particle types that may be included in panelsdisclosed herein.

FIG. 37A shows a schematic of a hollow magnetic particle.

FIG. 37B shows a schematic of a particle with hydrophobic pockets.

FIG. 37C shows a schematic of a shell-yolk SPION microgel hybridparticle.

FIG. 38 shows a schematic of particle surfaces engineered to captureproteins and/or peptides, adapted from Mol. Cells 2019, 42(5), 386-396.

DETAILED DESCRIPTION

Disclosed herein are compositions and methods of use thereof forassaying peptides and proteins in a sample in a simple and highthroughput manner. The present disclosure provides particle panels ofmultiple distinct particle types, which enrich proteins from a sampleonto distinct biomolecule coronas formed on the surface of the distinctparticle types. The particle panels disclosed herein can be used inmethods of corona analysis to detect thousands of proteins across a widedynamic range in the span of hours.

Currently, there are a small number of protein-based biomarkers in usetoday for clinical diagnosis, and in spite of extensive efforts toanalyze the plasma proteome for the expansion of markers, relatively fewnew candidates have been accepted as clinically useful tests. The plasmaproteome contains >10,000 proteins and potentially an order of magnitudemore protein isoforms with a concentration range spanning over 10 ordersof magnitude (from mg/mL to pg/mL). These attributes, combined with alack of convenient molecular tools for protein analytical work (such ascopy- or amplification-mechanisms), make comprehensive studies of theplasma proteome exceptionally challenging. Approaches to overcome thebroad dynamic range of proteins in biological samples are still forrobust identification and quantification against a background ofthousands of unique proteins and even more protein variants. However,there are no existing technologies that are capable of simultaneousmeasurement of proteins across the entire plasma concentration range ina format with a sufficient throughput and with a practical cost profileto allow for appropriately-sized studies with robust prospects forvalidation and replication. These challenges not only limit thediscovery of protein-based biomarkers of disease but have been abottleneck for the faster adoption of proteogenomics and proteinannotation of genomic variants. Advances in mass spectrometry (MS)approaches along with development of improved data analytics haveoffered tools for deep and broad proteomic analysis. Several attemptshave been made to substantially improve the detection of low abundanceproteins, such as depletion of highly abundant proteins, plasmafractionation, peptide fractionation, and isobaric labeling. However,current approaches are fairly complex and time-consuming (days toweeks), and thus require a tradeoff between depth of protein coverageand sample throughput. Consequently, a simple and robust strategy forcomprehensive and rapid analysis of the available body of information inthe proteome remains an unmet need.

Additionally, the earlier a disease is diagnosed, the more likely thatthe disease can be cured or successfully managed leading to a betterprognosis for the patient. When a disease is treated early, it may bepossible to prevent or delay problems from the disease and may improvethe outcomes for the patient, including extending the patient's lifeand/or quality of life. Early diagnosis of cancer is crucial, as manytypes of cancers can be successfully treated in their early stages. Forexample, five-year survival after early diagnosis and treatment ofbreast, ovarian, and lung cancers is 90%, 90%, and 70%, respectively,compared to 15%, 5%, and 10% for patients diagnosed at the most advancedstage of disease. Once cancer cells leave their tissue of origin,successful treatment using available established therapeutics becomesvery unlikely. Although recognizing the warning signs of cancers andtaking prompt action may lead to early diagnosis, the majority ofcancers (e.g., lung) show symptoms only after cancer cells have alreadyinvaded the surrounding tissues and metastasized throughout the body.For example, more than 60% of patients with breast, lung, colon, andovarian cancer have concealed or even metastatic colonies by the timetheir cancers are detected. Therefore, there is an urgent need fordevelopment of an effective approach for early detection of cancer. Suchan approach should have the sensitivity to identify a cancer at variousstages and the specificity to give a negative result when the personbeing tested is free of the cancer. There have been extensive efforts todevelop methods for early detection of cancers; although huge numbers ofrisk factors and biomarkers have been introduced, a broadly relevantplatform for early detection of a wide range of cancers remains elusive.As various types of cancers can change the composition of bloodplasma-even in their early stages-one promising approach for earlydetection is molecular blood analysis for biomarkers. Although thisstrategy has already worked for a few cancers (like PSA for prostatecancer), there are not yet specific biomarkers for early detection ofthe majority of cancers. For such cancers (e.g., lung), none of thedefined candidate circulating biomarkers has been clinically validated,and very few have reached late-stage clinical development. Therefore,there is an urgent need for novel approaches to improve our ability todetect cancer, as well as other diseases, at very early stages.

To meet the need for high throughput, simple assays for detectingproteins in a sample, which can be used to detect proteins associated aparticular disease at very early stages—the present disclosure providesparticle panels and methods for using said particle panels to assay forpeptides and proteins in a sample (e.g., a complex, biological samplesuch as plasma) in a simple, rapid, and high throughput manner. Inparticular, the present disclosure provides particle panels of multipledistinct particle types, which enrich proteins from a sample ontodistinct biomolecule coronas formed on the surface of the distinctparticle types. The particle types included in the particle panelsdisclosed herein are particularly well suited to enriching for a highnumber of proteins across a wide dynamic range in an unbiased fashion.The combinations of particle types selected for inclusion in a particlepanel of the present disclosure are varied in their physicochemicalproperties (e.g., size, surface charge, core material, shell material,surface chemistry, porosity, morphology, and other properties). However,particle types may also share several of said physicochemicalproperties. For example, a particle panel disclosed herein can include afirst particle type and a second particle type, wherein the firstparticle type and the second particle type share at least twophysicochemical properties and differ by at least two physicochemicalproperties, such that the first particle type and the second particletype are different. Importantly, the variation in at least onephysicochemical property between a first particle type and a secondparticle type of the particle panel can lead to the formation ofdistinct coronas, which form on the corresponding distinct particletypes. Thus, the selection of particle types for inclusion in a particlepanel for use in the methods disclosed herein enables the high number ofproteins that can be enriched across a wide dynamic range in a sample(e.g., plasma).

The particle panels of the present disclosure can include particle typesthat have magnetic properties, which allow for them to be easilyseparated after incubation in a complex biological sample. For example,the present disclosure provides superparamagnetic iron oxidenanoparticles (SPIONs), which have unique magnetic properties and canrapidly separate biomolecules, drug delivery and contrast agents inmagnetic resonance imaging (MRI). Superparamagnetic particles can have acore of solely iron oxide (e.g., an iron oxide core) or can have smalliron oxide crystals embedded in a polystyrene core.

The present disclosure provides methods that have been developed forsynthesis of SPIONs. In an example, a thermal decomposition of ironoleate in a nonpolar solvent can be used to synthesize small size(usually <30 nm) monodisperse magnetic nanocrystals with highcrystallinity. Those nanocrystals are hydrophobic and need to transferto aqueous phase through the ligand exchange or chemical modifications.SPIONs generated using this method can meet requirement(s) forbiomedical use. A solvothermal method is another way to synthesizeSPIONs by reduction of iron (III) chloride with ethylene glycol. Highlywater dispersible SPMNPs can be synthesized by a modified solvothermalapproach using hydrophilic ligands such as citrate, polyacrylic acid(PAA) and polyvinylpyrrolidone (PVP). The particle surfaces can befurther modified with different silanes which have functional groups viaa Stober process. The surface functionalities can also be achieved by afacile method through a surfactant-free seeded emulsion polymerizationto form SPMNP@polymer composite particles.

The present disclosure provides compositions, systems, and methods ofuse thereof for large-scale, high-throughput, efficient, andcost-effective proteomic profiling and machine learning. Disclosedherein is a scalable parallel protein identification and quantificationtechnology for assaying proteins in a sample using a particle panel withdistinct particle types to enrich proteins in distinct biomoleculecoronas formed on the distinct particle types. “Biomolecule corona” asused herein can be used referred to interchangeably with the term“protein corona,” and refers to the formation of a layer of proteins onthe surface of a particle after the particle has been contacted with asample (e.g., plasma). This method may be referred to interchangeably ascorona analysis or, in some examples, “Proteograph” analysis (depictedin FIG. 22), which combines a multi-particle type protein coronastrategy with mass spectrometry (MS). Particle types included in theparticle panels disclosed herein can be superparamagnetic and are, thus,rapidly separated or isolated from unbound protein (proteins that havenot adsorbed onto the surface of a particle to form the corona) in asample, after incubation of the particle in the sample.

To date, particles have been poorly characterized for high-throughputtranslation proteomic analysis, due to steps in processing the proteinsin the protein corona (e.g., centrifugation or membrane filtration toseparate corona proteins from free plasma proteins and washing to removeloosely attached proteins from particles), which confound data and lackreproducibility and accuracy. The corona analysis (e.g., “Proteograph”)methods disclosed herein can integrate overlapping but distinct particletype protein coronas with, for example, liquid chromatography-massspectrometry (LC-MS) for potential use in large-scale efficientproteomic profiling and machine learning. The particle type platform canbe unbiased (e.g., not limited to a predetermined analyte), and the MSdata acquisition platform can be unbiased in terms of analytemeasurement, both of which can be amenable to automation. The formationof a layer of proteins on the surface of particles upon their contactwith plasma, which is referred to as protein corona (FIG. 22A) isdisclosed herein as a method for identifying proteins. The compositionand quantity of the corona proteins can depend on the physiochemicalproperties of the particle type, and changes in these engineeredproperties can result in reproducibly different proteins in the coronain terms of identity and/or quantity.

In some embodiments, the particles disclosed herein can besuperparamagnetic iron oxide nanoparticles (SPIONS). SPIONs can bedistinct from one another by being synthesized to have distinct surfacechemistries. In some embodiments, SPIONs of different surfacechemistries can be combined in analysis of proteins formed on theirprotein coronas. SPIONs, other particle types, or a combination thereofcan be combined into panels of particle types that can be used forproteomic analysis of a sample.

In some embodiments, disclosed herein is a panel of three particle typeswith distinct surface chemistries, synthesized and used for theformation of protein corona, which can be rapidly separated by magnetfrom unbound proteins. Each particle type can reproducibly generate aunique protein corona pattern by capture of both high and low abundanceproteins. For instance, by integrating the distinct proteomic profilesgenerated from at least three particle types, greater than 1,500proteins of a single pooled colorectal cancer (CRC) plasma sample can beidentified of which many can be FDA-cleared/approved biomarkers. Forexample, in some embodiments a screen of three particle types can detectover 1,500 proteins, of which 65 are FDA-cleared/approved biomarkers.

In some embodiments, the corona analysis workflow with Proteograph (FIG.22B) can take ˜4-6 hours to prepare a batch of 96 corona samples for MSanalysis. The protein identification in the CRC pool can be done usingjust three total MS fractions analyzed in about one hour runs each,hence a total of about 3 hours MS time.

In some embodiments, three distinct particle types can be used identifygreater than 1,500 proteins from a single pooled plasma within 8 hoursincluding sample preparation and LC-MS, in contrast to less than 500proteins without using the compositions, systems, and methods disclosedherein for using particle type corona strategy.

The corona analysis technology can be used to identify a disease. Forexample, the particles and methods of use thereof disclosed herein canbe used to analyze serum samples from patients with non-small cell lungcancer (NSCLC) and age- and gender-matched healthy subjects. MS featurescan be discovered using this platform, including known and novelfeatures that can distinguish between NSCLC and control samples. Thus,the corona analysis platform technology is capable of larger and morerobust validation and replication studies. Moreover, the uniqueproperties of the corona analysis for high-throughput and unbiasedproteomic sampling can enable annotation of genomic data and applicationof machine learning classification methods. The multi-particle typeprotein corona-based platform technology described herein can facilitateefficient and comprehensive proteomics profiling, enabling larger-sizedstudies for biomarker discovery and validation.

In some embodiments, the compositions and methods of use thereofdisclosed herein exhibit high assay accuracy, as demonstrated byaddition of an increasing concentration of a reference, C-reactiveprotein (CRP), to plasma samples, and subsequent detection of CRP levelsin the protein coronas, showing a slope of 0.9 (95% CI 0.81-0.98) forCRP levels in particle corona versus spiked plasma. In some embodiments,the median precision of the platform in assay replicates can be 24 CV %across 8,738 measured MS features taken from three distinct particletype protein coronas.

SPIONs with distinct surface chemistries can be applied for proteincorona analysis of a single pooled plasma sample. For instance, as downin the present disclosure, the resulting proteomic data demonstratesthat increasing the number of particle types in a particle panel canresult in the identification of more proteins (particularly lowabundance proteins). The addition of more distinct particle types canlead to even broader and deeper proteome profiling. The compositions ofparticles disclosed herein and the panels of particle types comprisingsaid different particle types can be tailored to profile the proteome atdifferent levels of depth and breadth by varying the number and type ofparticles in the panels—analogous to different levels of coverage ingene sequencing.

The multi-particle type protein corona-based assay disclosed herein hasshown several equally important features for plasma proteome analysis.As compared to conventional proteomic techniques that usually involvetime-consuming depletion and fractionation workflows, the compositionsand methods of use disclosed herein can avoid those complicatedworkflows and can be much faster. Notably, the corona analysis assay canbe robustly automated, thus further increasing precision and reducingthe amount of time required for sample analysis in, for example, a96-well plate format. The corona analysis platform can sensitivelymeasure differences between samples, while reducing the dynamic range ofthose comparisons, thus enabling more comparisons to be observed. Thecorona analysis technology can identify new biomarkers without targetinga pre-determined set of proteins. The scalability and efficiency of thecorona analysis platform can be used for large proteomics studies, whichcan lead to deeper understanding of disease and biological mechanisms.For example, by adding proteomic data to multiomic data sets, andperforming machine learning analyses, novel classifications can begenerated and put into context genomic disease information that is notwell-understood today, such as single nucleotide polymorphism (SNP)variants, changes in DNA methylation patterns and splice variants.Additionally, the technology can be extended to other biological fluidssuch as cerebrospinal fluid, cell lysate, and even tissue homogenate forrapid, accurate and precise profiling of proteomes, which can facilitatethe discovery of new biomarkers for different diseases.

Methods for making superparamagnetic nanoparticles (SPMNPs) orsuperparamagnetic iron oxide particles (SPIONs) are disclosed herein.These particles and embodiments thereof can be used in a protein coronaassay.

The methods and systems of the present disclosure improve proteomicanalysis by simplifying sample preparation and MS data acquisition. Themethods and systems perform sample preparation in about 5 steps in about0.25 days and acquire MS data for about 12 fractions in about 0.5 daysper sample.

The present disclosure provides compositions and methods of use thereoffor assaying a sample for proteins. Compositions described hereininclude particle panels comprising one or more than one distinctparticle types. Particle panels described herein can vary in the numberof particle types and the diversity of particle types in a single panel.For example, particles in a panel may vary based on size,polydispersity, shape and morphology, surface charge, surface chemistryand functionalization, and base material. Panels may be incubated with asample to be analyzed for proteins and protein concentrations. Proteinsin the sample adsorb to the surface of the different particle types inthe particle panel to form a protein corona. The exact protein and theconcentration of protein that adsorbs to a certain particle type in theparticle panel may depend on the composition, size, and surface chargeof said particle type. Thus, each particle type in a panel may havedifferent protein coronas due to adsorbing a different set of proteins,different concentrations of a particular protein, or a combinationthereof. Each particle type in a panel may have mutually exclusiveprotein coronas or may have overlapping protein coronas. Overlappingprotein coronas can overlap in protein identity, in proteinconcentration, or both.

The present disclosure also provides methods for selecting a particletypes for inclusion in a panel depending on the sample type. Particletypes included in a panel may be a combination of particles that areoptimized for removal of highly abundant proteins. Particle types alsoconsistent for inclusion in a panel are those selected for adsorbingparticular proteins of interest. The particles can be nanoparticles. Theparticles can be microparticles. The particles can be a combination ofnanoparticles and microparticles.

The present disclosure provides a method for selecting particle panelsthat exhibit broad coverage of proteins in a biological sample (e.g., aplasma sample). Particles are selected for inclusion in a particle panelusing a combinatorial approach. Particles with a wide range ofphysicochemical properties are selected, for example, particles may varyby size, surface charge, core material, shell material, surfacechemistry, porosity, morphology, and other properties. However,particles may also share several of said physicochemical properties. Forexample, a particle panel disclosed herein can include a first particletype and a second particle type, wherein the first particle type and thesecond particle type share at least two physicochemical properties anddiffer by at least two physicochemical properties, such that the firstparticle type and the second particle type are different. A particlepanel disclosed herein can include a first particle type and a secondparticle type, wherein the first particle type and the second particletype share at least one physicochemical property and differ by at leasttwo physicochemical properties, such that the first particle type andthe second particle type are different. A particle panel disclosedherein can include a first particle type and a second particle type,wherein the first particle type and the second particle type share atleast two physicochemical properties and differ by at least onephysicochemical property, such that the first particle type and thesecond particle type are different. Non-limiting examples ofphysicochemical properties can include size, charge, core material,shell material, porosity, or surface hydrophobicity. Importantly, thevariation in at least one physicochemical property between a firstparticle type and a second particle type of the particle panel can leadto the formation of distinct coronas, which form on the correspondingdistinct particle types. For example, a first particle type and a secondparticle type, which vary in charge may each adsorb different proteins,different concentrations of the same proteins, or both differentproteins and different concentrations of the same proteins. Thus, thefirst particle type and the second particle type would have distinctbiomolecule coronas. Size is one example of a physicochemical propertythat may vary to yield this result. One or more than one physicochemicalproperties (e.g., size, charge, core material, shell material, porosity,or surface hydrophobicity, or any combination thereof) can vary to yielddistinct biomolecule coronas. Other optimization parameters forselection of particle types for a panel may include any particularannotation set, for example interactome, secretome, FDA markers,proteins with clinically relevant genetic polymorphisms. As seen inTABLE 10 and TABLE 12, more than one of these particles arenanoparticles, but are made of different polymer coatings. As anotherexample, more than one particle of TABLE 10 and TABLE 12 share similarsurface charge and similar size but are made of different materials. Asanother example, more than one particle of TABLE 10 and TABLE 12_exhibita porous surface. As another example, more than one particle of TABLE 10and TABLE 12 exhibit a non-porous surface. As another example, more thanone particle of TABLE 10 and TABLE 12 exhibit a carboxylate coatedsurface. As another example, more than one particle of TABLE 10 andTABLE 12 exhibit an amine coated surface. With all the many combinationsof particles and particle types that can be in a particle panel, it issurprising and unexpected that the particle panels disclosed herein werecapable of identifying a large number of proteins (e.g., plasmaproteins) in a sample (e.g., a plasma sample) over a wide dynamic rangein an unbiased manner, and were able to be used in a method of assayingproteins in a sample with a high level of reproducibility (e.g.,quantile normalized coefficient of variation <20%).

The present disclosure provides over 200 distinct particle types, withover 100 different surface chemistries and over 50 diverse physicalproperties. In particular, over 23 particle types have beencharacterized for use in a method of assaying proteins in a sample. Eachof these particle types can be combined with other particle types inpanels that are designed to optimally assay a particular protein ofinterest or to optimally identify biomarkers for a disease of interest.Panels can include any number of these particle types or any combinationof particle types, and the variety of particle types disclosed hereincan enable assaying and detection of a wide range of proteins withvarying physicochemical properties.

In some embodiments, the present disclosure provides method ofidentifying proteins in a sample, the method comprising: incubating apanel comprising a plurality of particle types with the sample to form aplurality of protein corona; digesting the plurality of protein coronasto generate proteomic data; and identifying proteins in the sample byquantifying the proteomic data.

Particle Materials

The particle panels disclosed herein may comprise particle types made ofa variety of different materials. Panels can be assembled with specifictypes of particles to identify a broad range of proteins in the sample,or to selectively assay for a particular protein or set of proteins ofinterest. The particle types may include, for example, nanoparticles(NPs), microparticles, magnetic particles (MPs), magnetic nanoparticles(MNPs), superparamagnetic iron oxide nanoparticles (SPIONs), orsuperparamagnetic nanoparticles (SPMNPs). Particles described herein maybe magnetic particles. Magnetic particles herein may besuperparamagnetic particles (SPMP), superparamagnetic nanoparticles(SPMNP), superparamagnetic iron oxide particles (SPIOP), orsuperparamagnetic iron oxide nanoparticles (SPION). In some cases, aSPMNP may be a SPION. Magnetism can be conferred via iron oxide core oriron oxide crystals grafted to particle. In some cases, we refer hereinto SPMNP, which is a magnetic particle. SPMNP can also be synthesized tobe a SPMP.

Particles can be made from various materials. For example, particles maybe made of a polymer, a lipid, a metal, silica, a protein, a nucleicacid, a small molecule, or a large molecule. For example, particlematerials consistent with the present disclosure include metals, metaloxides, magnetic materials, polymers, and lipids. Examples of metalmaterials include any one of or any combination of gold, silver, copper,nickel, cobalt, palladium, platinum, iridium, osmium, rhodium,ruthenium, rhenium, vanadium, chromium, manganese, niobium, molybdenum,tungsten, tantalum, iron and cadmium, or any other material described inU.S. Pat. No. 7,749,299. Metal oxide particles may be iron oxideparticles or titanium oxide particles. Magnetic particles may be ironoxide nanoparticles.

Examples of polymers include any one of or any combination ofpolyethylenes, polycarbonates, polyanhydrides, polyhydroxyacids,polypropylfumerates, polycaprolactones, polyamides, polyacetals,polyethers, polyesters, poly(orthoesters), polycyanoacrylates, polyvinylalcohols, polyurethanes, polyphosphazenes, polyacrylates,polymethacrylates, polycyanoacrylates, polyureas, polystyrenes, orpolyamines, a polyalkylene glycol (e.g., polyethylene glycol (PEG)), apolyester (e.g., poly(lactide-co-glycolide) (PLGA), polylactic acid, orpolycaprolactone), polystyrene, or a copolymer of two or more polymers,such as a copolymer of a polyalkylene glycol (e.g., PEG) and a polyester(e.g., PLGA). In some embodiments, the polymer is a lipid-terminatedpolyalkylene glycol and a polyester, or any other material disclosed inU.S. Pat. No. 9,549,901, which is incorporated by reference in itsentirety herein.

Examples of lipids that can be used to form the particles of the presentdisclosure include cationic, anionic, and neutrally charged lipids. Forexample, particles can be made of any one of or any combination ofdioleoylphosphatidylglycerol (DOPG), diacylphosphatidylcholine,diacylphosphatidylethanolamine, ceramide, sphingomyelin, cephalin,cholesterol, cerebrosides and diacylglycerols,dioleoylphosphatidylcholine (DOPC), dimyristoylphosphatidylcholine(DMPC), and dioleoylphosphatidylserine (DOPS), phosphatidylglycerol,cardiolipin, diacylphosphatidylserine, diacylphosphatidic acid,N-dodecanoyl phosphatidylethanolamines, N-succinylphosphatidylethanolamines, N-glutarylphosphatidylethanolamines,lysylphosphatidylglycerols, palmitoyloleyolphosphatidylglycerol (POPG),lecithin, lysolecithin, phosphatidylethanolamine,lysophosphatidylethanolamine, dioleoylphosphatidylethanolamine (DOPE),dipalmitoyl phosphatidyl ethanolamine (DPPE),dimyristoylphosphoethanolamine (DMPE),distearoyl-phosphatidyl-ethanolamine (DSPE),palmitoyloleoyl-phosphatidylethanolamine (POPE)palmitoyloleoylphosphatidylcholine (POPC), egg phosphatidylcholine(EPC), distearoylphosphatidylcholine (DSPC), dioleoylphosphatidylcholine(DOPC), dipalmitoylphosphatidylcholine (DPPC),dioleoylphosphatidylglycerol (DOPG), dipalmitoylphosphatidylglycerol(DPPG), palmitoyloleyolphosphatidylglycerol (POPG), 16-O-monomethyl PE,16-O-dimethyl PE, 18-1-trans PE,palmitoyloleoyl-phosphatidylethanolamine (POPE),1-stearoyl-2-oleoyl-phosphatidyethanolamine (SOPE), phosphatidylserine,phosphatidylinositol, sphingomyelin, cephalin, cardiolipin, phosphatidicacid, cerebrosides, dicetylphosphate, and cholesterol, or any othermaterial listed in U.S. Pat. No. 9,445,994, which is incorporated byreference in its entirety herein.

The particle panels disclosed herein may include specific particle typeshaving structures that are particularly amenable to sampling proteins ofa specific size, which are present in a sample. For example, theparticle panel may include a hollow magnetic particle, as shown in FIG.37A. The hollow magnetic particle comprises a nanoparticle with a hollowcore, wherein the nanoparticle shell is made of smaller primary ironoxide crystals. Hollow magnetic particles may be synthesized by anautoclave reaction based on hydrothermal treatment of FeCl₃, citrate,polyacrylamide or sodium polyacrylate, and urea as described in Cheng,Wei, et al. (“One-step synthesis of superparamagnetic monodisperseporous Fe₃O₄ hollow and core-shell spheres.” Journal of MaterialsChemistry 20.9 (2010): 1799-1805), which is incorporated herein byreference in its entirety. The nanoparticle shell made of the smallerprimary iron oxide crystals is porous, allowing the generation ofprotein coronas on the surface of the nanoparticle via size exclusiveeffects. Binding surfaces can be primarily inside pores, thus preventinglarge proteins from diffusing into the particle and binding. In somecases, large proteins may still bind to the outside and included in theoverall corona, but these hollow particles can enrich smaller proteinsover larger ones. These hollow magnetic particles may be included in anyparticle panel described herein and is still easily separated fromunbound protein using a magnet.

As another example, the particle panel may include a nanoparticle with ahydrophobic pocket as shown in FIG. 37B. The nanoparticle with ahydrophobic pocket is particularly amenable for sampling a molecule orprotein of a specific solubility (e.g., a poorly soluble protein), whichare present in a sample. A nanoparticle with a hydrophobic pocket has astructure of a superparamagnetic iron oxide core and additionally has apolymeric coating. Nanoparticles with hydrophobic pockets can besynthesized using an autoclave reaction for SPION core synthesis, asdescribed elsewhere herein, followed by a free radical polymerizationfor synthesizing a poly(glycidyl methacrylate) coating. This may befurther followed by post-synthetic modification of the surface withhydrophobic amines, such as a benzyl amine moiety. These nanoparticleswith a hydrophobic pocket are particularly well suited to capturingsmall molecules for metabolomics. The pore size of the nanoparticle canbe modulated to exclude proteins to that only small molecules interactwith the hydrophobic pockets. In some embodiments, pore size can betuned during synthesis. For example, the synthesis may include swellingthe particle to create porosity and the degree of swelling can impactthe pore size. As another example, an erosion technique can be used tocarve out different pores in a particle type with a more solid surface.Protein targets may also be sampled using the nanoparticles with ahydrophobic pocket.

In another example, the particle panel may include a shell-yolk SPIONmicrogel hybrid nanoparticle, as shown in FIG. 37C. A shell-yolk SPIONmicrogel hybrid nanoparticle has a structure of a SPION exterior and amicrogel interior. Shell-yolk SPION microgel hybrid nanoparticles can besynthesized using an autoclave reaction based on hydrothermal treatmentof FeCl₃, citrate, polyacrylamide or sodium polyacrylate, and urea asdescribed in Cheng et al. (“One-step synthesis of superparamagneticmonodisperse porous Fe₃O₄ hollow and core-shell spheres.” Journal ofMaterials Chemistry 20.9 (2010): 1799-1805) and Zou et al. (“Facilesynthesis of highly water-dispersible and monodispersed Fe₃O₄ hollowmicrospheres and their application in water treatment.” RSC Advances3.45 (2013): 23327-23334). In a second step of the synthesis, a hydrogelis formed inside the hollow nanoparticle.

In another example, the particle panel may include a particle surfacethat is engineered to capture proteins and/or peptides. A primary layerof proteins may form an initial corona on the particle surface eithernon-covalently or covalently. Direct non-covalent interactions with theparticle surface may include ionic bonds, hydrophobic bonds, andhydrogen bonds. Additionally, these particles, which can be initiallymodified with small chemical entities, can bind other proteins in theplasma after formation of a primary protein corona. Modulating thechemical modifications present at the surface of the particle can beemployed to tune the non-covalent interactions between the particlesurface and peptides and/or proteins in a sample. Alternatively, oradditionally, the solution phase composition (e.g., pH) can be modulatedto tune the non-covalent interactions between the particles and peptidesand/or proteins in a sample. Covalent coupling of proteins and/orpeptides to the surface of particles may be performed by introducing areactive group (e.g., an NHS ester, a maleimide, a carboxylate, etc.) tothe surface of the particle. Thus, an NHS-ester bearing particle may beparticularly amenable to sampling one or more proteins in solution.Alternatively, a coupling reagent that can sample and bind proteins outof a complex mixture may be a photoinitiated reactant group, which canreact with proteins only after exposure to photons of a given wavelength(e.g., UV) and intensity. Advantages of utilizing particlesfunctionalized with said photoinitiated reactive group can include, butare not limited to: (1) locking in the primary protein corona withoutloss of proteins during the step of washing the particles and/or loss ofproteins due to displacement by higher affinity proteins, (2) capturingproteins with intermediate binding affinities because the covalentlinkage can be established at any time during the binding process, (3)generating a wider range of surfaces than would result at equilibrium,and (4) generating surfaces with certain protein stoichiometry by use ofdefined protein mixtures (instead of plasma) for binding. A definedprotein mixture can be any synthetically-derived or purified mixture ofproteins, obtained or made recombinantly or isolated and then mixed witha defined stoichiometry. For example, for a particular protein ofinterest for protein-protein interactions (e.g., ubiquitin), the definedprotein mixture may be a simple solution of that protein. As anotherexample, the defined protein mixture can be a purified or enriched setof proteins from a certain class of interest (e.g., glycosylatedproteins). Particles may also be modified to be functionalized with onehalf of a click-chemistry reaction pair and unnatural point mutatedproteins in a sample contain an amino acid with the other half of theclick-chemistry reaction pair. Particles and other proteins in a samplecan be functionalized with one half of a click-chemistry reaction pairand unnatural point mutated proteins in a sample contain an amino acidwith the other half of the click-chemistry reaction pair. In thepresence of a chemical catalyst (e.g., copper) or light (e.g., aphotoinitiated click chemistry reagent), the reaction is carried outleading to linkage of particles to the unnatural point mutated proteinsin the sample and/or linkage of the unnatural point mutated proteins inthe sample and other proteins in the samples. For example, the methodmay start with a simple solution of proteins that may contain a mutatedprotein that is enriched in the sample. The main advantage of thesesystems is that the surface of the particles can be engineered withrespect to stoichiometry, protein/surface orientation, andprotein/protein orientation. This allows for engineering of durablesurfaces that can withstand assay steps described elsewhere herein(e.g., extensive washing). For example, proteins with specific unnaturalamino acids at a location of interest in the protein sequence can beintroduced as described in Lee et al. (Mol Cells. 2019 May 31;42(5):386-396. doi: 10.14348/molcells.2019.0078.). The unnatural aminoacid that is introduced into the protein can have one half of theclick-chemistry pair that can react to the other half of theclick-chemistry pair on the surface of the particle. In this manner, therather than random adsorption of proteins to the surface of a particleto form the corona, these modified proteins can bind to the surface ofthe particle in a particular orientation. The result is that the coronathat forms on the surface of a particle type can be tuned with theproteins engineered in a specific 3-D orientation that is controlled.This same general methodology can be used to link protein complexes tothe surface of the particle type. Here, one or more subunits of thecomplex can be modified to be covalently linked together then linked tothe surface of a particle type either in a second step of the synthesisor using a different chemistry, which can be performed later in theparticle surface modification process. A schematic is shown in FIG. 38.Particles that have a surface engineered to non-covalently or covalentlycapture proteins and/or peptides may still be SPION or polymer-modifiedSPION particles, which can be synthesized in a variety of ways includingstandard SPION synthesis via solvothermal methods, ligand exchangeprocesses, silica coating processes, and/or activation or installationof a specific reagent coupling strategy. Advantages of these systemsinclude bio-surface generation, exploitation of interactome, anddirected corona assembly.

As another example, the particle panel may include functionalizedparticles for histone capture. For example, these particles may beanionic. Additionally, or alternatively, the assay conditions may beoptimized to enrich for histones in a sample and labeling techniques maybe optimized to improve mass spectrometry detection for histones andpost translational modifications. These functionalized particles forhistone capture may be made of polymers, silica, target ligand, and orany combination thereof. Functionalized particles can be synthesizedusing a variety of approaches including standard SPION synthesis viasolvothermal methods, ligand exchange processes, surface-initiatedpolymerization. Anionic particles surfaces can be synthesized byfunctionalizing the surfaces with polymers such as polycarboxylate,various ligand such as dendrimers, branched ligand, or carboxylatederivatives, and/or sulfanilamide acids. Optimization of assayconditions may include buffering the binding solution to a pH of about9. While many proteins would exhibit diminished binding to an anionicsurface at this pH, histones are basic proteins with a primary sequencehaving a pI˜11 (e.g., H4_Human pI˜11.3, H3_Human pI˜11) and, this, willremain almost entirely positively charged. As a result, under basic pHconditions, histones can strongly interact with particle surfaces viaionic bonds and can, thus, be selectively enriched on particle surfaces.Additionally, histones may be selectively enriched on particle surfacesby modifying particle surfaces with histone binding proteins. Thus, ahistone protein corona can be obtained using the particles disclosedherein. Optimizing labelingmethodologiescanhelpsimplifytheinterpretationofmassspectrometrydataandallowforimprovements in post-translational modification (PTM) identification andassignment. As the type, location, and occupancy of a post-translationalmodification on a histone can be different and can help regulate geneexpression, these particles can be useful for selectively enrichinghistones in a sample and interrogate the PTM, thus providing informationabout the how genes in a sample are being regulated. This may in turnhelp inform various disease conditions in which regulation of, geneexpression is aberrant.

Examples of particle types consistent with the present disclosure areshown in FIG. 21 and in TABLE 1 below.

TABLE 1 Particle Types P# Description HX-13 or SP-001 Carboxylate(Citrate) HX-19 or SP-002 Phenol-formaldehyde resin coated HX-31 orSP-004 Polystyrene coated HX-38 or SP-005 CarboxylatedPoly(styrene-co-methacrylic acid), P(St-co-MAA) HX-42 or SP-006N-(3-Trimethoxysilylpropyl)diethylenetriamine coated HX-57 or SP-0081,2,4,5-Benzenetetracarboxylic acid coated HX-58 or SP-009Vinylbenzyltrimethylammonium chloride (PVBTMAC) coated HX-59 or SP-010Carboxylated, Poly(acrylic acid), PAA SP-333 Carboxylate microparticle,surfactant free SP-339 Polystyrene carboxyl functionalized SP-341Carboxylic acid, 150 nm SP-347 Silica coated, 200 nm SP-348 Carboxylicacid SP-353 Amino surface microparticle, 0.4-0.6 μm SP-356 Silica aminofunctionalized microparticle, 0.1-0.39 μm SP-363 Jeffamine surface,0.1-0.39 μm SP-364 Polystyrene microparticle, 2.0-2.9 μm SP-365 SilicaSP-369 Carboxylated Original coating, 50 nm SP-373 Dextran basedcoating, 0.13 μm SP-374 Silica Silanol coated with lower acidity HX-20or SP-003 Silica-coated superparamagnetic iron oxide NPs (SPION) HX-56or SP-007 poly(N-(3-(dimethylamino)propyl) methacrylamide)(PDMAPMA)-coated SPION HX-86 or SP-011 poly(oligo(ethylene glycol)methyl ether methacrylate) (POEGMA)-coated SPION

Properties of Particles

Particles that are consistent with the present disclosure can be madeand used in methods of forming protein coronas after incubation in abiofluid at a wide range of sizes. For example, the particles disclosedherein can have a diameter of at least 10 nm, at least 100 nm, at least200 nm, at least 300 nm, at least 400 nm, at least 500 nm, at least 600nm, at least 700 nm, at least 800 nm, at least 900 nm, at least 1000 nm,at least 1100 nm, at least 1200 nm, at least 1300 nm, at least 1400 nm,at least 1500 nm, at least 1600 nm, at least 1700 nm, at least 1800 nm,at least 1900 nm, at least 2000 nm, at least 2100 nm, at least 2200 nm,at least 2300 nm, at least 2400 nm, at least 2500 nm, at least 2600 nm,at least 2700 nm, at least 2800 nm, at least 2900 nm, at least 3000 nm,at least 3100 nm, at least 3200 nm, at least 3300 nm, at least 3400 nm,at least 3500 nm, at least 3600 nm, at least 3700 nm, at least 3800 nm,at least 3900 nm, at least 4000 nm, at least 4100 nm, at least 4200 nm,at least 4300 nm, at least 4400 nm, at least 4500 nm, at least 4600 nm,at least 4700 nm, at least 4800 nm, at least 4900 nm, at least 5000 nm,at least 5100 nm, at least 5200 nm, at least 5300 nm, at least 5400 nm,at least 5500 nm, at least 5600 nm, at least 5700 nm, at least 5800 nm,at least 5900 nm, at least 6000 nm, at least 6100 nm, at least 6200 nm,at least 6300 nm, at least 6400 nm, at least 6500 nm, at least 6600 nm,at least 6700 nm, at least 6800 nm, at least 6900 nm, at least 7000 nm,at least 7100 nm, at least 7200 nm, at least 7300 nm, at least 7400 nm,at least 7500 nm, at least 7600 nm, at least 7700 nm, at least 7800 nm,at least 7900 nm, at least 8000 nm, at least 8100 nm, at least 8200 nm,at least 8300 nm, at least 8400 nm, at least 8500 nm, at least 8600 nm,at least 8700 nm, at least 8800 nm, at least 8900 nm, at least 9000 nm,at least 9100 nm, at least 9200 nm, at least 9300 nm, at least 9400 nm,at least 9500 nm, at least 9600 nm, at least 9700 nm, at least 9800 nm,at least 9900 nm, at least 10000 nm or from 10 nm to 50 nm, from 50 nmto 100 nm, from 100 nm to 150 nm, from 150 nm to 200 nm, from 200 nm to250 nm, from 250 nm to 300 nm, from 300 nm to 350 nm, from 350 nm to 400nm, from 400 nm to 450 nm, from 450 nm to 500 nm, from 500 nm to 550 nm,from 550 nm to 600 nm, from 600 nm to 650 nm, from 650 nm to 700 nm,from 700 nm to 750 nm, from 750 nm to 800 nm, from 800 nm to 850 nm,from 850 nm to 900 nm, from 100 nm to 300 nm, from 150 nm to 350 nm,from 200 nm to 400 nm, from 250 nm to 450 nm, from 300 nm to 500 nm,from 350 nm to 550 nm, from 400 nm to 600 nm, from 450 nm to 650 nm,from 500 nm to 700 nm, from 550 nm to 750 nm, from 600 nm to 800 nm,from 650 nm to 850 nm, from 700 nm to 900 nm, or from 10 nm to 900 nm,from 10 to 100 nm, from 100 to 200 nm, from 200 to 300 nm, from 300 to400 nm, from 400 to 500 nm, from 500 to 600 nm, from 600 to 700 nm, from700 to 800 nm, from 800 to 900 nm, from 900 to 1000 nm, from 1000 to1100 nm, from 1100 to 1200 nm, from 1200 to 1300 nm, from 1300 to 1400nm, from 1400 to 1500 nm, from 1500 to 1600 nm, from 1600 to 1700 nm,from 1700 to 1800 nm, from 1800 to 1900 nm, from 1900 to 2000 nm, from2000 to 2100 nm, from 2100 to 2200 nm, from 2200 to 2300 nm, from 2300to 2400 nm, from 2400 to 2500 nm, from 2500 to 2600 nm, from 2600 to2700 nm, from 2700 to 2800 nm, from 2800 to 2900 nm, from 2900 to 3000nm, from 3000 to 3100 nm, from 3100 to 3200 nm, from 3200 to 3300 nm,from 3300 to 3400 nm, from 3400 to 3500 nm, from 3500 to 3600 nm, from3600 to 3700 nm, from 3700 to 3800 nm, from 3800 to 3900 nm, from 3900to 4000 nm, from 4000 to 4100 nm, from 4100 to 4200 nm, from 4200 to4300 nm, from 4300 to 4400 nm, from 4400 to 4500 nm, from 4500 to 4600nm, from 4600 to 4700 nm, from 4700 to 4800 nm, from 4800 to 4900 nm,from 4900 to 5000 nm, from 5000 to 5100 nm, from 5100 to 5200 nm, from5200 to 5300 nm, from 5300 to 5400 nm, from 5400 to 5500 nm, from 5500to 5600 nm, from 5600 to 5700 nm, from 5700 to 5800 nm, from 5800 to5900 nm, from 5900 to 6000 nm, from 6000 to 6100 nm, from 6100 to 6200nm, from 6200 to 6300 nm, from 6300 to 6400 nm, from 6400 to 6500 nm,from 6500 to 6600 nm, from 6600 to 6700 nm, from 6700 to 6800 nm, from6800 to 6900 nm, from 6900 to 7000 nm, from 7000 to 7100 nm, from 7100to 7200 nm, from 7200 to 7300 nm, from 7300 to 7400 nm, from 7400 to7500 nm, from 7500 to 7600 nm, from 7600 to 7700 nm, from 7700 to 7800nm, from 7800 to 7900 nm, from 7900 to 8000 nm, from 8000 to 8100 nm,from 8100 to 8200 nm, from 8200 to 8300 nm, from 8300 to 8400 nm, from8400 to 8500 nm, from 8500 to 8600 nm, from 8600 to 8700 nm, from 8700to 8800 nm, from 8800 to 8900 nm, from 8900 to 9000 nm, from 9000 to9100 nm, from 9100 to 9200 nm, from 9200 to 9300 nm, from 9300 to 9400nm, from 9400 to 9500 nm, from 9500 to 9600 nm, from 9600 to 9700 nm,from 9700 to 9800 nm, from 9800 to 9900 nm, from 9900 to 10000 nm. Thediameter can be measured by dynamic light scattering (DLS) as anindirect measure of size. The DLS measurement can be an‘intensity-weighted’ average, which means the size distribution that themean is calculated from can be weighted by the sixth power of radius.This can be referred to herein as ‘z-average’ or ‘intensity-mean’.

Alternatively, particles disclosed herein can have a radius of at least10 nm, at least 100 nm, at least 200 nm, at least 300 nm, at least 400nm, at least 500 nm, at least 600 nm, at least 700 nm, at least 800 nm,at least 900 nm, at least 1000 nm, at least 1100 nm, at least 1200 nm,at least 1300 nm, at least 1400 nm, at least 1500 nm, at least 1600 nm,at least 1700 nm, at least 1800 nm, at least 1900 nm, at least 2000 nm,at least 2100 nm, at least 2200 nm, at least 2300 nm, at least 2400 nm,at least 2500 nm, at least 2600 nm, at least 2700 nm, at least 2800 nm,at least 2900 nm, at least 3000 nm, at least 3100 nm, at least 3200 nm,at least 3300 nm, at least 3400 nm, at least 3500 nm, at least 3600 nm,at least 3700 nm, at least 3800 nm, at least 3900 nm, at least 4000 nm,at least 4100 nm, at least 4200 nm, at least 4300 nm, at least 4400 nm,at least 4500 nm, at least 4600 nm, at least 4700 nm, at least 4800 nm,at least 4900 nm, at least 5000 nm, at least 5100 nm, at least 5200 nm,at least 5300 nm, at least 5400 nm, at least 5500 nm, at least 5600 nm,at least 5700 nm, at least 5800 nm, at least 5900 nm, at least 6000 nm,at least 6100 nm, at least 6200 nm, at least 6300 nm, at least 6400 nm,at least 6500 nm, at least 6600 nm, at least 6700 nm, at least 6800 nm,at least 6900 nm, at least 7000 nm, at least 7100 nm, at least 7200 nm,at least 7300 nm, at least 7400 nm, at least 7500 nm, at least 7600 nm,at least 7700 nm, at least 7800 nm, at least 7900 nm, at least 8000 nm,at least 8100 nm, at least 8200 nm, at least 8300 nm, at least 8400 nm,at least 8500 nm, at least 8600 nm, at least 8700 nm, at least 8800 nm,at least 8900 nm, at least 9000 nm, at least 9100 nm, at least 9200 nm,at least 9300 nm, at least 9400 nm, at least 9500 nm, at least 9600 nm,at least 9700 nm, at least 9800 nm, at least 9900 nm, at least 10000 nmor from 10 nm to 50 nm, from 50 nm to 100 nm, from 100 nm to 150 nm,from 150 nm to 200 nm, from 200 nm to 250 nm, from 250 nm to 300 nm,from 300 nm to 350 nm, from 350 nm to 400 nm, from 400 nm to 450 nm,from 450 nm to 500 nm, from 500 nm to 550 nm, from 550 nm to 600 nm,from 600 nm to 650 nm, from 650 nm to 700 nm, from 700 nm to 750 nm,from 750 nm to 800 nm, from 800 nm to 850 nm, from 850 nm to 900 nm,from 100 nm to 300 nm, from 150 nm to 350 nm, from 200 nm to 400 nm,from 250 nm to 450 nm, from 300 nm to 500 nm, from 350 nm to 550 nm,from 400 nm to 600 nm, from 450 nm to 650 nm, from 500 nm to 700 nm,from 550 nm to 750 nm, from 600 nm to 800 nm, from 650 nm to 850 nm,from 700 nm to 900 nm, or from 10 nm to 900 nm, from 10 to 100 nm, from100 to 200 nm, from 200 to 300 nm, from 300 to 400 nm, from 400 to 500nm, from 500 to 600 nm, from 600 to 700 nm, from 700 to 800 nm, from 800to 900 nm, from 900 to 1000 nm, from 1000 to 1100 nm, from 1100 to 1200nm, from 1200 to 1300 nm, from 1300 to 1400 nm, from 1400 to 1500 nm,from 1500 to 1600 nm, from 1600 to 1700 nm, from 1700 to 1800 nm, from1800 to 1900 nm, from 1900 to 2000 nm, from 2000 to 2100 nm, from 2100to 2200 nm, from 2200 to 2300 nm, from 2300 to 2400 nm, from 2400 to2500 nm, from 2500 to 2600 nm, from 2600 to 2700 nm, from 2700 to 2800nm, from 2800 to 2900 nm, from 2900 to 3000 nm, from 3000 to 3100 nm,from 3100 to 3200 nm, from 3200 to 3300 nm, from 3300 to 3400 nm, from3400 to 3500 nm, from 3500 to 3600 nm, from 3600 to 3700 nm, from 3700to 3800 nm, from 3800 to 3900 nm, from 3900 to 4000 nm, from 4000 to4100 nm, from 4100 to 4200 nm, from 4200 to 4300 nm, from 4300 to 4400nm, from 4400 to 4500 nm, from 4500 to 4600 nm, from 4600 to 4700 nm,from 4700 to 4800 nm, from 4800 to 4900 nm, from 4900 to 5000 nm, from5000 to 5100 nm, from 5100 to 5200 nm, from 5200 to 5300 nm, from 5300to 5400 nm, from 5400 to 5500 nm, from 5500 to 5600 nm, from 5600 to5700 nm, from 5700 to 5800 nm, from 5800 to 5900 nm, from 5900 to 6000nm, from 6000 to 6100 nm, from 6100 to 6200 nm, from 6200 to 6300 nm,from 6300 to 6400 nm, from 6400 to 6500 nm, from 6500 to 6600 nm, from6600 to 6700 nm, from 6700 to 6800 nm, from 6800 to 6900 nm, from 6900to 7000 nm, from 7000 to 7100 nm, from 7100 to 7200 nm, from 7200 to7300 nm, from 7300 to 7400 nm, from 7400 to 7500 nm, from 7500 to 7600nm, from 7600 to 7700 nm, from 7700 to 7800 nm, from 7800 to 7900 nm,from 7900 to 8000 nm, from 8000 to 8100 nm, from 8100 to 8200 nm, from8200 to 8300 nm, from 8300 to 8400 nm, from 8400 to 8500 nm, from 8500to 8600 nm, from 8600 to 8700 nm, from 8700 to 8800 nm, from 8800 to8900 nm, from 8900 to 9000 nm, from 9000 to 9100 nm, from 9100 to 9200nm, from 9200 to 9300 nm, from 9300 to 9400 nm, from 9400 to 9500 nm,from 9500 to 9600 nm, from 9600 to 9700 nm, from 9700 to 9800 nm, from9800 to 9900 nm, from 9900 to 10000 nm.

In certain examples, the particles disclosed herein have a diameter of100 nm to 400 nm. In other examples, the particles disclosed herein havea radius of 100 nm to 400 nm. Particle size can be determined by anumber of techniques, such as dynamic light scattering or electronmicroscopy (e.g., SEM, TEM). Particles disclosed herein can benanoparticles or microparticles.

Additionally, particles can have a homogenous size distribution or aheterogeneous size distribution. Polydispersity index (PDI), which canbe measured by techniques such as dynamic light scattering, is a measureof the size distribution. A low PDI indicates a more homogeneous sizedistribution and a higher PDI indicates a more heterogeneous sizedistribution. For example, particles disclosed herein can have a PDI ofless than 0.5, less than 0.4, less than 0.3, less than 0.2, less than0.15, or less than 0.1. In particular embodiments, the particlesdisclosed herein have a PDI of less than 0.1.

Particles disclosed herein can have a range of different surfacecharges. Particles can be negatively charged, positively charged, orneutral in charge. In some embodiments, particles have a surface chargeof −500 mV to −450 mV, −450 mV to −400 mV, −400 mV to −350 mV, −350 mVto −300 mV, −300 mV to −250 mV, −250 mV to −200 mV, −200 mV to −150 mV,−150 mV to −100 mV, −100 mV to −90 mV, −90 mV to −80 mV, −80 mV to −70mV, −70 mV to −60 mV, −60 mV to −50 mV, −50 mV to −40 mV, −40 mV to −30mV, −30 mV to −20 mV, −20 mV to −10 mV, −10 mV to 0 mV, 0 mV to 10 mV,10 mV to 20 mV, 20 mV to 30 mV, 30 mV to 40 mV, 40 mV to 50 mV, 50 mV to60 mV, 60 mV to 70 mV, 70 mV to 80 mV, 80 mV to 90 mV, 90 mV to 100 mV,100 mV to 110 mV, 110 mV to 120 mV, 120 mV to 130 mV, 130 mV to 140 mV,140 mV to 150 mV, 150 mV to 200 mV, 200 mV to 250 mV, 250 mV to 300 mV,300 mV to 350 mV, 350 mV to 400 mV, 400 mV to 450 mV, 450 mV to 500 mV,−500 my to −400 mV, −400 my to −300 mV, −300 my to −200 mV, −200 my to−100 mV, −100 my to 0 mV, 0 my to 100 mV, 100 my to 200 mV, 200 my to300 mV, 300 my to 400 mV, or 400 my to 500 mV. In particular examples,particles disclosed herein have a surface charge of −60 mV to 60 mV.

Various particle morphologies are consistent with the particle types inpanels of the present disclosure. For example, particles may bespherical, colloidal, cube shaped, square shaped, rods, wires, cones,pyramids, and oblong. Particles of the present disclosure may be solidparticles, porous particles, or meso porous particles. Particles mayhave a small surface area or a large surface area. Particles may havevaried magnetic properties that can be measured by SQUID, whichdetermines magnetism in response to an external field. In some cases,particles have a core-shell or yolk-shell structure.

Particle Panels

The particle panels disclosed herein can be used to identifying a numberof proteins, peptides, or protein groups using the Proteograph workflowdescribed herein (MS analysis of distinct biomolecule coronascorresponding to distinct particle types in the particle panel). Featureintensities, as disclosed herein, refers to the intensity of a discretespike (“feature”) seen on a plot of mass to charge ratio versusintensity from a mass spectrometry run of a sample. These features cancorrespond to variably ionized fragments of peptides and/or proteins.Using the data analysis methods described herein, feature intensitiescan be sorted into protein groups. Protein groups refer to two or moreproteins that are identified by a shared peptide sequence.Alternatively, a protein group can refer to one protein that isidentified using a unique identifying sequence. For example, if in asample, a peptide sequence is assayed that is shared between twoproteins (Protein 1: XYZZX and Protein 2: XYZYZ), a protein group couldbe the “XYZ protein group” having two members (protein 1 and protein 2).Alternatively, if the peptide sequence is unique to a single protein(Protein 1), a protein group could be the “ZZX” protein group having onemember (Protein 1). Each protein group can be supported by more than onepeptide sequence. Protein detected or identified according to theinstant disclosure can refer to a distinct protein detected in thesample (e.g., distinct relative other proteins detected using massspectrometry). Thus, analysis of proteins present in distinct coronascorresponding to the distinct particle types in a particle panel, yieldsa high number of feature intensities. This number decreases as featureintensities are processed into distinct peptides, further decreases asdistinct peptides are processed into distinct proteins, and furtherdecreases as peptides are grouped into protein groups (two or moreproteins that share a distinct peptide sequence).

The particle panels disclosed herein can be used to identify at least atleast 100 proteins, at least 200 proteins, at least 300 proteins, atleast 400 proteins, at least 500 proteins, at least 600 proteins, atleast 700 proteins, at least 800 proteins, at least 900 proteins, atleast 1000 proteins, at least 1100 proteins, at least 1200 proteins, atleast 1300 proteins, at least 1400 proteins, at least 1500 proteins, atleast 1600 proteins, at least 1700 proteins, at least 1800 proteins, atleast 1900 proteins, at least 2000 proteins, at least 2100 proteins, atleast 2200 proteins, at least 2300 proteins, at least 2400 proteins, atleast 2500 proteins, at least 2600 proteins, at least 2700 proteins, atleast 2800 proteins, at least 2900 proteins, at least 3000 proteins, atleast 3100 proteins, at least 3200 proteins, at least 3300 proteins, atleast 3400 proteins, at least 3500 proteins, at least 3600 proteins, atleast 3700 proteins, at least 3800 proteins, at least 3900 proteins, atleast 4000 proteins, at least 4100 proteins, at least 4200 proteins, atleast 4300 proteins, at least 4400 proteins, at least 4500 proteins, atleast 4600 proteins, at least 4700 proteins, at least 4800 proteins, atleast 4900 proteins, at least 5000 proteins, at least 10000 proteins, atleast 20000 proteins, at least 50000 proteins, at least 100000 proteins,from 100 to 5000 proteins, from 200 to 4700 proteins, from 300 to 4400proteins, from 400 to 4100 proteins, from 500 to 3800 proteins, from 600to 3500 proteins, from 700 to 3200 proteins, from 800 to 2900 proteins,from 900 to 2600 proteins, from 1000 to 2300 proteins, from 1000 to 3000proteins, from 3000 to 4000 proteins, from 4000 to 5000 proteins, from5000 to 6000 proteins, from 6000 to 7000 proteins, from 7000 to 8000proteins, from 8000 to 9000 proteins, from 9000 to 10000 proteins, from10000 to 11000 proteins, from 11000 to 12000 proteins, from 12000 to13000 proteins, from 13000 to 14000 proteins, from 14000 to 15000proteins, from 15000 to 16000 proteins, from 16000 to 17000 proteins,from 17000 to 18000 proteins, from 18000 to 19000 proteins, from 19000to 20000 proteins, from 20000 to 25000 proteins, from 25000 to 30000proteins, from 10000 to 20000 proteins, from 10000 to 50000 proteins,from 20000 to 100000 proteins, from 2000 to 20000 proteins, from 1800 to20000 proteins, or from 10000 to 100000 proteins.

The particle panels disclosed herein can be used to identify at least atleast 100 protein groups, at least 200 protein groups, at least 300protein groups, at least 400 protein groups, at least 500 proteingroups, at least 600 protein groups, at least 700 protein groups, atleast 800 protein groups, at least 900 protein groups, at least 1000protein groups, at least 1100 protein groups, at least 1200 proteingroups, at least 1300 protein groups, at least 1400 protein groups, atleast 1500 protein groups, at least 1600 protein groups, at least 1700protein groups, at least 1800 protein groups, at least 1900 proteingroups, at least 2000 protein groups, at least 2100 protein groups, atleast 2200 protein groups, at least 2300 protein groups, at least 2400protein groups, at least 2500 protein groups, at least 2600 proteingroups, at least 2700 protein groups, at least 2800 protein groups, atleast 2900 protein groups, at least 3000 protein groups, at least 3100protein groups, at least 3200 protein groups, at least 3300 proteingroups, at least 3400 protein groups, at least 3500 protein groups, atleast 3600 protein groups, at least 3700 protein groups, at least 3800protein groups, at least 3900 protein groups, at least 4000 proteingroups, at least 4100 protein groups, at least 4200 protein groups, atleast 4300 protein groups, at least 4400 protein groups, at least 4500protein groups, at least 4600 protein groups, at least 4700 proteingroups, at least 4800 protein groups, at least 4900 protein groups, atleast 5000 protein groups, at least 10000 protein groups, at least 20000protein groups, at least 100000 protein groups, from 100 to 5000 proteingroups, from 200 to 4700 protein groups, from 300 to 4400 proteingroups, from 400 to 4100 protein groups, from 500 to 3800 proteingroups, from 600 to 3500 protein groups, from 700 to 3200 proteingroups, from 800 to 2900 protein groups, from 900 to 2600 proteingroups, from 1000 to 2300 protein groups, from 1000 to 3000 proteingroups, from 3000 to 4000 protein groups, from 4000 to 5000 proteingroups, from 5000 to 6000 protein groups, from 6000 to 7000 proteingroups, from 7000 to 8000 protein groups, from 8000 to 9000 proteingroups, from 9000 to 10000 protein groups, from 10000 to 11000 proteingroups, from 11000 to 12000 protein groups, from 12000 to 13000 proteingroups, from 13000 to 14000 protein groups, from 14000 to 15000 proteingroups, from 15000 to 16000 protein groups, from 16000 to 17000 proteingroups, from 17000 to 18000 protein groups, from 18000 to 19000 proteingroups, from 19000 to 20000 protein groups, from 20000 to 25000 proteingroups, from 25000 to 30000 protein groups, from 10000 to 20000 proteingroups, from 10000 to 50000 protein groups, from 20000 to 100000 proteingroups, from 2000 to 20000 protein groups, from 1800 to 20000 proteingroups, or from 10000 to 100000 protein groups.

The particle panels disclosed herein can be used to identify the numberof distinct proteins disclosed herein, and/or any of the specificproteins disclosed herein, over a wide dynamic range. For example, theparticle panels disclosed herein comprising distinct particle types, canenrich for proteins in a sample, which can be identified using theProteograph workflow, over the entire dynamic range at which proteinsare present in a sample (e.g., a plasma sample). In some embodiments, aparticle panel including any number of distinct particle types disclosedherein, enriches and identifies proteins over a dynamic range of atleast 2. In some embodiments, a particle panel including any number ofdistinct particle types disclosed herein, enriches and identifiesproteins over a dynamic range of at least 3. In some embodiments, aparticle panel including any number of distinct particle types disclosedherein, enriches and identifies proteins over a dynamic range of atleast 4. In some embodiments, a particle panel including any number ofdistinct particle types disclosed herein, enriches and identifiesproteins over a dynamic range of at least 5. In some embodiments, aparticle panel including any number of distinct particle types disclosedherein, enriches and identifies proteins over a dynamic range of atleast 6. In some embodiments, a particle panel including any number ofdistinct particle types disclosed herein, enriches and identifiesproteins over a dynamic range of at least 7. In some embodiments, aparticle panel including any number of distinct particle types disclosedherein, enriches and identifies proteins over a dynamic range of atleast 8. In some embodiments, a particle panel including any number ofdistinct particle types disclosed herein, enriches and identifiesproteins over a dynamic range of at least 9. In some embodiments, aparticle panel including any number of distinct particle types disclosedherein, enriches and identifies proteins over a dynamic range of atleast 10. In some embodiments, a particle panel including any number ofdistinct particle types disclosed herein, enriches and identifiesproteins over a dynamic range of at least 11. In some embodiments, aparticle panel including any number of distinct particle types disclosedherein, enriches and identifies proteins over a dynamic range of atleast 12. In some embodiments, a particle panel including any number ofdistinct particle types disclosed herein, enriches and identifiesproteins over a dynamic range of at least 13. In some embodiments, aparticle panel including any number of distinct particle types disclosedherein, enriches and identifies proteins over a dynamic range of atleast 14. In some embodiments, a particle panel including any number ofdistinct particle types disclosed herein, enriches and identifiesproteins over a dynamic range of at least 15. In some embodiments, aparticle panel including any number of distinct particle types disclosedherein, enriches and identifies proteins over a dynamic range of atleast 20. In some embodiments, a particle panel including any number ofdistinct particle types disclosed herein, enriches and identifiesproteins over a dynamic range of from 2 to 100. In some embodiments, aparticle panel including any number of distinct particle types disclosedherein, enriches and identifies proteins over a dynamic range of from 2to 20. In some embodiments, a particle panel including any number ofdistinct particle types disclosed herein, enriches and identifiesproteins over a dynamic range of from 2 to 10. In some embodiments, aparticle panel including any number of distinct particle types disclosedherein, enriches and identifies proteins over a dynamic range of from 2to 5. In some embodiments, a particle panel including any number ofdistinct particle types disclosed herein, enriches and identifiesproteins over a dynamic range of from 5 to 10.

A particle panel including any number of distinct particle typesdisclosed herein, enriches and identifies a single protein or proteingroup. In some embodiments, the single protein or protein group maycomprise proteins having different post-translational modifications. Forexample, a first particle type in the particle panel may enrich aprotein or protein group having a first post-translational modification,a second particle type in the particle panel may enrich the same proteinor same protein group having a second post-translational modification,and a third particle type in the particle panel may enrich the sameprotein or same protein group lacking a post-translational modification.In some embodiments, the particle panel including any number of distinctparticle types disclosed herein, enriches and identifies a singleprotein or protein group by binding different domains, sequences, orepitopes of the single protein or protein group. For example, a firstparticle type in the particle panel may enrich a protein or protein bybinding to a first domain of the protein or protein group, and a secondparticle type in the particle panel may enrich the same protein or sameprotein group by binding to a second domain of the protein or proteingroup.

In some embodiments, particle panels can have more than one particletypes. Increasing the number of particle types in a panel can be amethod for increasing the number of proteins that can be identified in agiven sample. An example of how increasing panel size may increase thenumber of identified proteins is shown in FIG. 11. For example, as shownin FIG. 11, a panel size of one particle type identified 419 uniqueproteins, a panel size of two particle types identified 588 proteins, apanel size of three particle types identified 727 proteins, a panel sizeof four particle types identified 844 proteins, a panel size of fiveparticle types identified 934 proteins, a panel size of six particletypes identified 1008 proteins, a panel size of seven particle typesidentified 1075 proteins, a panel size of eight particle typesidentified 1133 proteins, a panel size of nine particle types identified1184 proteins, a panel size of 10 particle types identified 1230proteins, a panel size of 11 particle types identified 1275 proteins,and a panel size of 12 particle types identified 1318 proteins. Theparticle types may include nanoparticle types.

In some embodiments, a panel size of one particle type is capable ofidentifying 200 to 600 unique proteins. In some embodiments, a panelsize of two particle types is capable of identifying 300 to 700 uniqueproteins. In some embodiments, a panel size of three particle types iscapable of identifying 500 to 900 unique proteins. In some embodiments,a panel size of four particle types is capable of identifying 600 to1000 unique proteins. In some embodiments, a panel size of five particletypes is capable of identifying 700 to 1100 unique proteins. In someembodiments, a panel size of six particle types is capable ofidentifying 800 to 1200 unique proteins. In some embodiments, a panelsize of seven particle types is capable of identifying 850 to 1250unique proteins. In some embodiments, a panel size of eight particletypes is capable of identifying 900 to 1300 unique proteins. In someembodiments, a panel size of nine particle types is capable ofidentifying 950 to 1350 unique proteins. In some embodiments, a panelsize of 10 particle types is capable of identifying 1000 to 1400 uniqueproteins. In some embodiments, a panel size of 11 particle types iscapable of identifying 1050 to 1450 unique proteins. In someembodiments, a panel size of 12 particle types is capable of identifying1100 to 1500 unique proteins. The particle types may includenanoparticle types.

The present disclosure provides over 200 distinct particle types, withover 100 different surface chemistries, and over 50 diverse physicalproperties. Distinct particle types comprise at least onephysicochemical property that differs between a first particle type anda second particle type. For example, the present disclosure provides aparticle panel having at least 2 distinct particle types, at least 3distinct particle types, at least 4 distinct particle types, at least 5distinct particle types, at least 6 distinct particle types, at least 7distinct particle types, at least 8 distinct particle types, at least 9distinct particle types, at least 10 distinct particle types, at least11 distinct particle types, at least 12 distinct particle types, atleast 13 distinct particle types, at least 14 distinct particle types,at least 15 distinct particle types, at least 20 distinct particletypes, at least 25 distinct particle types, at least 30 distinctparticle types, at least 35 distinct particle types, at least 40distinct particle types, at least 45 distinct particle types, at least50 distinct particle types, at least 100 distinct particle types, atleast 150 distinct particle types, at least 200 distinct particle types,at least 250 distinct particle types, at least 300 distinct particletypes, at least 350 distinct particle types, at least 400 distinctparticle types, at least 450 distinct particle types, at least 500distinct particle types, from 2 to 500 distinct particle types, from 2to 5 distinct particle types, from 5 to 10 distinct particle types, from10 to 15 distinct particle types, from 15 to 20 distinct particle types,from 20 to 40 distinct particle types, from 40 to 60 distinct particletypes, from 60 to 80 distinct particle types, from 80 to 100 distinctparticle types, from 100 to 500 distinct particle types, from 4 to 15distinct particle types, or from 2 to 20 distinct particle types. Theparticle types may include nanoparticle types.

In some embodiments, the present disclosure provide a panel size of atleast 1 particle distinct type, at least 2 distinct particle types, atleast 3 distinct particle types, at least 4 distinct particle types, atleast 5 distinct particle types, at least 6 distinct particle types, atleast 7 distinct particle types, at least 8 distinct particle types, atleast 9 distinct particle types, at least 10 distinct particle types, atleast 11 distinct particle types, at least 12 distinct particle types,at least 13 distinct particle types, at least 14 distinct particletypes, at least 15 distinct particle types, at least 16 distinctparticle types, at least 17 distinct particle types, at least 18distinct particle types, at least 19 distinct particle types, at least20 distinct particle types, at least 25 distinct particle types, atleast 30 distinct particle types, at least 35 distinct particle types,at least 40 distinct particle types, at least 45 distinct particletypes, at least 50 distinct particle types, at least 55 distinctparticle types, at least 60 distinct particle types, at least 65distinct particle types, at least 70 distinct particle types, at least75 distinct particle types, at least 80 distinct particle types, atleast 85 distinct particle types, at least 90 distinct particle types,at least 95 distinct particle types, at least 100 distinct particletypes, from 1 to 5 distinct particle types, from 5 to 10 distinctparticle types, from 10 to 15 distinct particle types, from 15 to 20distinct particle types, from 20 to 25 distinct particle types, from 25to 30 distinct particle types, from 30 to 35 distinct particle types,from 35 to 40 distinct particle types, from 40 to 45 distinct particletypes, from 45 to 50 distinct particle types, from 50 to 55 distinctparticle types, from 55 to 60 distinct particle types, from 60 to 65distinct particle types, from 65 to 70 distinct particle types, from 70to 75 distinct particle types, from 75 to 80 distinct particle types,from 80 to 85 distinct particle types, from 85 to 90 distinct particletypes, from 90 to 95 distinct particle types, from 95 to 100 distinctparticle types, from 1 to 100 distinct particle types, from 20 to 40distinct particle types, from 5 to 10 distinct particle types, from 3 to7 distinct particle types, from 2 to 10 distinct particle types, from 6to 15 distinct particle types, or from 10 to 20 distinct particle types.In particular embodiments, the present disclosure provides a panel sizeof from 3 to 10 particle types. In particular embodiments, the presentdisclosure provides a panel size of from 4 to 11 distinct particletypes. In particular embodiments, the present disclosure provides apanel size of from 5 to 15 distinct particle types. In particularembodiments, the present disclosure provides a panel size of from 5 to15 distinct particle types. In particular embodiments, the presentdisclosure provides a panel size of from 8 to 12 distinct particletypes. In particular embodiments, the present disclosure provides apanel size of from 9 to 13 distinct particle types. In particularembodiments, the present disclosure provides a panel size of 10 distinctparticle types. The particle types may include nanoparticle types.

For example, the present disclosure provides a particle panel having atleast 2 distinct particle types, at least 3 different surfacechemistries, at least 4 different surface chemistries, at least 5different surface chemistries, at least 6 different surface chemistries,at least 7 different surface chemistries, at least 8 different surfacechemistries, at least 9 different surface chemistries, at least 10different surface chemistries, at least 11 different surfacechemistries, at least 12 different surface chemistries, at least 13different surface chemistries, at least 14 different surfacechemistries, at least 15 different surface chemistries, at least 20different surface chemistries, at least 25 different surfacechemistries, at least 30 different surface chemistries, at least 35different surface chemistries, at least 40 different surfacechemistries, at least 45 different surface chemistries, at least 50different surface chemistries, at least 100 different surfacechemistries, at least 150 different surface chemistries, at least 200different surface chemistries, at least 250 different surfacechemistries, at least 300 different surface chemistries, at least 350different surface chemistries, at least 400 different surfacechemistries, at least 450 different surface chemistries, at least 500different surface chemistries, from 2 to 500 different surfacechemistries, from 2 to 5 different surface chemistries, from 5 to 10different surface chemistries, from 10 to 15 different surfacechemistries, from 15 to 20 different surface chemistries, from 20 to 40different surface chemistries, from 40 to 60 different surfacechemistries, from 60 to 80 different surface chemistries, from 80 to 100different surface chemistries, from 100 to 500 different surfacechemistries, from 4 to 15 different surface chemistries, or from 2 to 20different surface chemistries.

The present disclosure provides a particle panel having at least 2different surface chemistries, at least 3 different surface chemistries,at least 4 different surface chemistries, at least 5 different surfacechemistries, at least 6 different surface chemistries, at least 7different surface chemistries, at least 8 different surface chemistries,at least 9 different surface chemistries, at least 10 different surfacechemistries, at least 11 different surface chemistries, at least 12different surface chemistries, at least 13 different surfacechemistries, at least 14 different surface chemistries, at least 15different surface chemistries, at least 20 different surfacechemistries, at least 25 different surface chemistries, at least 30different surface chemistries, at least 35 different surfacechemistries, at least 40 different surface chemistries, at least 45different surface chemistries, at least 50 different surfacechemistries, at least 100 different surface chemistries, at least 150different surface chemistries, at least 200 different surfacechemistries, at least 250 different surface chemistries, at least 300different surface chemistries, at least 350 different surfacechemistries, at least 400 different surface chemistries, at least 450different surface chemistries, at least 500 different surfacechemistries, from 2 to 500 different surface chemistries, from 2 to 5different surface chemistries, from 5 to 10 different surfacechemistries, from 10 to 15 different surface chemistries, from 15 to 20different surface chemistries, from 20 to 40 different surfacechemistries, from 40 to 60 different surface chemistries, from 60 to 80different surface chemistries, from 80 to 100 different surfacechemistries, from 100 to 500 different surface chemistries, from 4 to 15different surface chemistries, or from 2 to 20 different surfacechemistries.

The present disclosure provides a particle panel having at least 2different physical properties, at least 3 different physical properties,at least 4 different physical properties, at least 5 different physicalproperties, at least 6 different physical properties, at least 7different physical properties, at least 8 different physical properties,at least 9 different physical properties, at least 10 different physicalproperties, at least 11 different physical properties, at least 12different physical properties, at least 13 different physicalproperties, at least 14 different physical properties, at least 15different physical properties, at least 20 different physicalproperties, at least 25 different physical properties, at least 30different physical properties, at least 35 different physicalproperties, at least 40 different physical properties, at least 45different physical properties, at least 50 different physicalproperties, at least 100 different physical properties, at least 150different physical properties, at least 200 different physicalproperties, at least 250 different physical properties, at least 300different physical properties, at least 350 different physicalproperties, at least 400 different physical properties, at least 450different physical properties, at least 500 different physicalproperties, from 2 to 500 different physical properties, from 2 to 5different physical properties, from 5 to 10 different physicalproperties, from 10 to 15 different physical properties, from 15 to 20different physical properties, from 20 to 40 different physicalproperties, from 40 to 60 different physical properties, from 60 to 80different physical properties, from 80 to 100 different physicalproperties, from 100 to 500 different physical properties, from 4 to 15different physical properties, or from 2 to 20 different physicalproperties.

In some embodiments, panels that optimally identity proteins andassociate biomarkers with diseases include panels selected from theparticle types described in TABLE 1. For example, a panel that optimallyidentifies proteins and associate biomarkers with diseases includepanels comprising SP-339, HX74, SP-356, SP-333, HX20, SP-374, HX42,SP-003, SP-007, and SP-011. Particle panels especially suitable toidentifying high numbers of proteins (e.g., greater than 1500 proteins)in a sample include from 5 to 10 distinct particle types in an assay.The number of distinct particle types included in a particle panel canbe tuned for a specific application (e.g., detection of a particularsubset of proteins or detection of a group of markers associated with aparticular disease). In some embodiments, panels with physicochemicallydistinct particle types that optimally identify proteins and associatebiomarkers with diseases include silica-coated SPIONs, acrylamide-basedSPIONs, and acrylate-based SPIONS. For example, a panel of particlesdisclosed herein that generates information rich proteomic data viatheir protein coronas, which can be associated with biomarkers anddiseases with high sensitivity and specificity include silica-coatedSPIONs (SP-003), poly(N-(3-(dimethylamino)propyl) methacrylamide)(PDMAPMA)-coated SPIONs (SP-007), and poly(oligo(ethylene glycol) methylether methacrylate) (POEGMA)-coated SPIONs (SP-011).

In some embodiments, the entire assay time from a single pooled plasma,including sample preparation and LC-MS, can be about 8 hours. In someembodiments, the entire assay time from a single pooled plasma,including sample preparation and LC-MS, can be about at least 1 hour, atleast 2 hours, at least 3 hours, at least 4 hours, at least 5 hours, atleast 6 hours, at least 7 hours, at least 8 hours, at least 9 hours, atleast 10 hours, under 20 hours, under 19 hours, under 18 hours, under 17hours, under 16 hours, under 15 hours, under 14 hours, under 13 hours,under 12 hours, under 11 hours, under 10 hours, under 9 hours, under 8hours, under 7 hours, under 6 hours, under 5 hours, under 4 hours, under3 hours, under 2 hours, under 1 hour, at least 5 min to 10 min, at least10 min to 20 min, at least 20 min to 30 min, at least 30 min to 40 min,at least 40 min to 50 min, at least 50 min to 60 min, at least 1 hour to1.5 hours, at least 1.5 hour to 2 hours, at least 2 hour to 2.5 hours,at least 2.5 hour to 3 hours, at least 3 hour to 3.5 hours, at least 3.5hour to 4 hours, at least 4 hour to 4.5 hours, at least 4.5 hour to 5hours, at least 5 hour to 5.5 hours, at least 5.5 hour to 6 hours, atleast 6 hour to 6.5 hours, at least 6.5 hour to 7 hours, at least 7 hourto 7.5 hours, at least 7.5 hour to 8 hours, at least 8 hour to 8.5hours, at least 8.5 hour to 9 hours, at least 9 hour to 9.5 hours, or atleast 9.5 hour to 10 hours. Early Stage Detection

The panels and methods of use thereof described herein can be used fordetection of markers in a sample from a subject, which are consistentwith a particular disease state. As shown in FIG. 18A and FIG. 18B,early stage cancers can be separated up to 8 years before symptomsdevelop. The Golestan Cohort enrolled 50,000 healthy subjects between2004 and 2008. As shown in FIG. 18A, banked plasma from enrollment wastested. 8 years after enrollment, approximately 1000 patients developedcancers. FIG. 18B illustrates classification of three cancers, Brain,Lung and Pancreatic from banked plasma, are classified by principlecomponent analysis. Here, the corona results are plotted against thethree principle component axes, showing three distinct statisticalpopulations. corona analysis (involving measurement of multipleproperties in order to profile a plurality of proteins) of banked plasmafrom enrollment date accurately classified cancers for 15 out of 15subjects examined (5 patients each for 3 cancers). In some embodiments,the panels of the present disclosure can be used to diagnose a diseasestate up to one year prior, up to two years prior, up to three yearsprior, up to four years prior, up to five years prior, up to six yearsprior, up to seven years prior, up to eight years prior, up to nineyears prior, up to 10 years prior, up to 15 years prior, up to 20 yearsprior, or up to 25 years prior to development of symptoms of thatdisease state.

The panels of the present disclosure can be used to detect a wide rangeof disease states in a given sample. For example, the panels of thepresent disclosure can be used to detect a cancer. The cancer may bebrain cancer, lung cancer, pancreatic cancer, glioblastoma, meningioma,myeloma, or pancreatic cancer. Corona analysis signals for these cancersare shown in FIG. 17B and FIG. 18.

The panels of the present disclosure can additionally be used to detectother cancers, such as any one of the cancers listed onhttps://www.cancer.gov/types, including acute lymphoblastic leukemia(ALL); acute myeloid leukemia (AML); cancer in adolescents;adrenocortical carcinoma; childhood adrenocortical carcinoma; unusualcancers of childhood; AIDS-related cancers; kaposi sarcoma (soft tissuesarcoma); AIDS-related lymphoma (lymphoma); primary cns lymphoma(lymphoma); anal cancer; appendix cancer—see gastrointestinal carcinoidtumors; astrocytomas, childhood (brain cancer); atypicalteratoid/rhabdoid tumor, childhood, central nervous system (braincancer); basal cell carcinoma of the skin—see skin cancer; bile ductcancer; bladder cancer; childhood bladder cancer; bone cancer (includesewing sarcoma and osteosarcoma and malignant fibrous histiocytoma);brain tumors; breast cancer; childhood breast cancer; bronchial tumors,childhood; burkitt lymphoma—see non-hodgkin lymphoma; carcinoid tumor(gastrointestinal); childhood carcinoid tumors; carcinoma of unknownprimary; childhood carcinoma of unknown primary; cardiac (heart) tumors,childhood; central nervous system; atypical teratoid/rhabdoid tumor,childhood (brain cancer); embryonal tumors, childhood (brain cancer);germ cell tumor, childhood (brain cancer); primary cns lymphoma;cervical cancer; childhood cervical cancer; childhood cancers; cancersof childhood, unusual; cholangiocarcinoma—see bile duct cancer;chordoma, childhood; chronic lymphocytic leukemia (CLL); chronicmyelogenous leukemia (CML); chronic myeloproliferative neoplasms;colorectal cancer; childhood colorectal cancer; craniopharyngioma,childhood (brain cancer); cutaneous t-cell lymphoma—see lymphoma(mycosis fungoides and sézary syndrome); ductal carcinoma in situ(DCIS)—see breast cancer; embryonal tumors, central nervous system,childhood (brain cancer); endometrial cancer (uterine cancer);ependymoma, childhood (brain cancer); esophageal cancer; childhoodesophageal cancer; esthesioneuroblastoma (head and neck cancer); ewingsarcoma (bone cancer); extracranial germ cell tumor, childhood;extragonadal germ cell tumor; eye cancer; childhood intraocularmelanoma; intraocular melanoma; retinoblastoma; fallopian tube cancer;fibrous histiocytoma of bone, malignant, and osteosarcoma; gallbladdercancer; gastric (stomach) cancer; childhood gastric (stomach) cancer;gastrointestinal carcinoid tumor; gastrointestinal stromal tumors (GIST)(soft tissue sarcoma); childhood gastrointestinal stromal tumors; germcell tumors; childhood central nervous system germ cell tumors (braincancer); childhood extracranial germ cell tumors; extragonadal germ celltumors; ovarian germ cell tumors; testicular cancer; gestationaltrophoblastic disease; hairy cell leukemia; head and neck cancer; hearttumors, childhood; hepatocellular (liver) cancer; histiocytosis,langerhans cell; hodgkin lymphoma; hypopharyngeal cancer (head and neckcancer); intraocular melanoma; childhood intraocular melanoma; isletcell tumors, pancreatic neuroendocrine tumors; kaposi sarcoma (softtissue sarcoma); kidney (renal cell) cancer; langerhans cellhistiocytosis; laryngeal cancer (head and neck cancer); leukemia; lipand oral cavity cancer (head and neck cancer); liver cancer; lung cancer(non-small cell and small cell); childhood lung cancer; lymphoma; malebreast cancer; malignant fibrous histiocytoma of bone and osteosarcoma;melanoma; childhood melanoma; melanoma, intraocular (eye); childhoodintraocular melanoma; merkel cell carcinoma (skin cancer); mesothelioma,malignant; childhood mesothelioma; metastatic cancer; metastaticsquamous neck cancer with occult primary (head and neck cancer); midlinetract carcinoma with nut gene changes; mouth cancer (head and neckcancer); multiple endocrine neoplasia syndromes; multiple myeloma/plasmacell neoplasms; mycosis fungoides (lymphoma); myelodysplastic syndromes,myelodysplastic/myeloproliferative neoplasms; myelogenous leukemia,chronic (cml); myeloid leukemia, acute (AML); myeloproliferativeneoplasms, chronic; nasal cavity and paranasal sinus cancer (head andneck cancer); nasopharyngeal cancer (head and neck cancer);neuroblastoma; non-hodgkin lymphoma; non-small cell lung cancer; oralcancer, lip and oral cavity cancer and oropharyngeal cancer (head andneck cancer); osteosarcoma and malignant fibrous histiocytoma of bone;ovarian cancer; childhood ovarian cancer; pancreatic cancer; childhoodpancreatic cancer; pancreatic neuroendocrine tumors (islet cell tumors);papillomatosis (childhood laryngeal); paraganglioma; childhoodparaganglioma; paranasal sinus and nasal cavity cancer (head and neckcancer); parathyroid cancer; penile cancer; pharyngeal cancer (head andneck cancer); pheochromocytoma; childhood pheochromocytoma; pituitarytumor; plasma cell neoplasm/multiple myeloma; pleuropulmonary blastoma;pregnancy and breast cancer; primary central nervous system (CNS)lymphoma; primary peritoneal cancer; prostate cancer; rectal cancer;recurrent cancer; renal cell (kidney) cancer; retinoblastoma;rhabdomyosarcoma, childhood (soft tissue sarcoma); salivary gland cancer(head and neck cancer); sarcoma; childhood rhabdomyosarcoma (soft tissuesarcoma); childhood vascular tumors (soft tissue sarcoma); ewing sarcoma(bone cancer); kaposi sarcoma (soft tissue sarcoma); osteosarcoma (bonecancer); soft tissue sarcoma; uterine sarcoma; sézary syndrome(lymphoma); skin cancer; childhood skin cancer; small cell lung cancer;small intestine cancer; soft tissue sarcoma; squamous cell carcinoma ofthe skin—see skin cancer; squamous neck cancer with occult primary,metastatic (head and neck cancer); stomach (gastric) cancer; childhoodstomach (gastric) cancer; t-cell lymphoma, cutaneous—see lymphoma(mycosis fungoides and sézary syndrome); testicular cancer; childhoodtesticular cancer; throat cancer (head and neck cancer); nasopharyngealcancer; oropharyngeal cancer; hypopharyngeal cancer; thymoma and thymiccarcinoma; thyroid cancer; transitional cell cancer of the renal pelvisand ureter (kidney (renal cell) cancer); carcinoma of unknown primary;childhood cancer of unknown primary; unusual cancers of childhood;ureter and renal pelvis, transitional cell cancer (kidney (renal cell)cancer; urethral cancer; uterine cancer, endometrial; uterine sarcoma;vaginal cancer; childhood vaginal cancer; vascular tumors (soft tissuesarcoma); vulvar cancer; wilms tumor and other childhood kidney tumors;or cancer in young adults. Further, the particle panels of the presentdisclosure can be used to detect other disease, such as Alzheimer'sdisease and multiple sclerosis.

Sample

The panels of the present disclosure can be used to generate proteomicdata from protein coronas and subsequently associated with any of thebiological states described herein. Samples consistent with the presentdisclosure include biological samples from a subject. The subject may bea human or a non-human animal. Biological samples may be a biofluid. Forexample, the biofluid may be plasma, serum, CSF, urine, tear, celllysates, tissue lysates, cell homogenates, tissue homogenates, nippleaspirates, fecal samples, synovial fluid and whole blood, or saliva.Samples can also be non-biological samples, such as water, milk,solvents, or anything homogenized into a fluidic state. Said biologicalsamples can contain a plurality of proteins or proteomic data, which maybe analyzed after adsorption of proteins to the surface of the variousparticle types in a panel and subsequent digestion of protein coronas.Proteomic data can comprise nucleic acids, peptides, or proteins. Any ofthe samples herein can contain a number of different analytes, which canbe analyzed using the compositions and methods disclosed herein. Theanalytes can be proteins, peptides, small molecules, nucleic acids,metabolites, lipids, or any molecule that could potentially bind orinteract with the surface of a particle type.

Disclosed herein are compositions and methods for multi-omic analysis.“Multi-omic(s)” or “multiomic(s)” can refer to an analytical approachfor analyzing biomolecules at a large scale, wherein the data sets aremultiple omes, such as proteome, genome, transcriptome, lipidome, andmetabolome. Non-limiting examples of multi-omic data includes proteomicdata, genomic data, lipidomic data, glycomic data, transcriptomic data,or metabolomics data. “Biomolecule” in “biomolecule corona” can refer toany molecule or biological component that can be produced by, or ispresent in, a biological organism. Non-limiting examples of biomoleculesinclude proteins (protein corona), polypeptides, polysaccharides, asugar, a lipid, a lipoprotein, a metabolite, an oligonucleotide, anucleic acid (DNA, RNA, micro RNA, plasmid, single stranded nucleicacid, double stranded nucleic acid), metabolome, as well as smallmolecules such as primary metabolites, secondary metabolites, and othernatural products, or any combination thereof. In some embodiments, thebiomolecule is selected from the group of proteins, nucleic acids,lipids, and metabolomes.

In some embodiments, a sample of the present disclosure can be aplurality of samples. At least two samples of the plurality of samplescan be spatially isolated. Spatially isolated refers to samples that arecontained in separate volumes. For example, spatially isolated samplescan refer to samples that are in separate wells in a plate or separatetubes. Spatially isolated samples can refer to samples that are inseparate wells in a plate or separate tubes and assayed together on thesame instrument. In some embodiments, the present disclosure providesparticle panels and methods of use thereof that are compatible withanalyzing a plurality of samples, such as at least 2 spatially isolatedsamples, at least 5 spatially isolated samples, at least 10 spatiallyisolated samples, at least 15 spatially isolated samples, at least 20spatially isolated samples, at least 25 spatially isolated samples, atleast 30 spatially isolated samples, at least 35 spatially isolatedsamples, at least 40 spatially isolated samples, at least 45 spatiallyisolated samples, at least 50 spatially isolated samples, at least 55spatially isolated samples, at least 60 spatially isolated samples, atleast 65 spatially isolated samples, at least 70 spatially isolatedsamples, at least 75 spatially isolated samples, at least 80 spatiallyisolated samples, at least 85 spatially isolated samples, at least 90spatially isolated samples, at least 95 spatially isolated samples, atleast 96 spatially isolated samples, at least 100 spatially isolatedsamples, at least 120 spatially isolated samples, at least 140 spatiallyisolated samples, at least 160 spatially isolated samples, at least 180spatially isolated samples, at least 200 spatially isolated samples, atleast 220 spatially isolated samples, at least 240 spatially isolatedsamples, at least 260 spatially isolated samples, at least 280 spatiallyisolated samples, at least 300 spatially isolated samples, at least 320spatially isolated samples, at least 340 spatially isolated samples, atleast 360 spatially isolated samples, at least 380 spatially isolatedsamples, at least 400 spatially isolated samples, at least 420 spatiallyisolated samples, at least 440 spatially isolated samples, at least 460spatially isolated samples, at least 480 spatially isolated samples, atleast 500 spatially isolated samples, at least 600 spatially isolatedsamples, at least 700 spatially isolated samples, at least 800 spatiallyisolated samples, at least 900 spatially isolated samples, at least 1000spatially isolated samples, at least 1100 spatially isolated samples, atleast 1200 spatially isolated samples, at least 1300 spatially isolatedsamples, at least 1400 spatially isolated samples, at least 1500spatially isolated samples, at least 1600 spatially isolated samples, atleast 1700 spatially isolated samples, at least 1800 spatially isolatedsamples, at least 1900 spatially isolated samples, at least 2000spatially isolated samples, at least 5000 spatially isolated samples, atleast 10000 spatially isolated samples, from 2 to 10 spatially isolatedsamples, from 2 to 100 spatially isolated samples, from 2 to 200spatially isolated samples, from 2 to 300 spatially isolated samples,from 50 to 150 spatially isolated samples, from 10 to 20 spatiallyisolated samples, from 20 to 30 spatially isolated samples, from 30 to40 spatially isolated samples, from 40 to 50 spatially isolated samples,from 50 to 60 spatially isolated samples, from 60 to 70 spatiallyisolated samples, from 70 to 80 spatially isolated samples, from 80 to90 spatially isolated samples, from 90 to 100 spatially isolatedsamples, from 100 to 150 spatially isolated samples, from 150 to 200spatially isolated samples, from 200 to 250 spatially isolated samples,from 250 to 300 spatially isolated samples, from 300 to 350 spatiallyisolated samples, from 350 to 400 spatially isolated samples, from 400to 450 spatially isolated samples, from 450 to 500 spatially isolatedsamples, from 500 to 600 spatially isolated samples, from 600 to 700spatially isolated samples, from 700 to 800 spatially isolated samples,from 800 to 900 spatially isolated samples, from 900 to 1000 spatiallyisolated samples, from 1000 to 2000 spatially isolated samples, from2000 to 3000 spatially isolated samples, from 3000 to 4000 spatiallyisolated samples, from 4000 to 5000 spatially isolated samples, from5000 to 6000 spatially isolated samples, from 6000 to 7000 spatiallyisolated samples, from 7000 to 8000 spatially isolated samples, from8000 to 9000 spatially isolated samples, or from 9000 to 10000 spatiallyisolated samples.

The methods disclosed herein include isolating a particle panel (havinga plurality of particle types) from one or more than one sample. Theparticle panels having particle types that are superparamagnetic can berapidly isolated or separated from the sample using a magnetic.Moreover, multiple samples that are spatially isolated can be processedin parallel. Thus, the methods disclosed herein provide for isolating orseparating a particle panel from unbound protein in a plurality ofspatially isolated panels at the same time, by using a magnet. Forexample, particle panels may be incubated with a plurality of spatiallyisolated samples, wherein each spatially isolated sample is in a well ina well plate (e.g., a 96-well plate). After incubation, the particlepanels in each of the wells of the well plate can be separated fromunbound protein present in the spatially isolated samples by placing theentire plate on a magnet. This simultaneously pulls down thesuperparamagnetic particles in the particle panel. The supernatant ineach well can be removed to remove the unbound protein. These steps(incubate, pull down using a magnet) can be repeated to effectively washthe particles, thus removing residual background unbound protein thatmay be present in a sample. This is one example, but one of skill in theart could envision numerous other scenarios in which superparamagneticparticles are rapidly isolated from one or more than one spatiallyisolated samples at the same time.

In some embodiments, the panels of the present disclosure providesidentification and measurement of particular proteins in the biologicalsamples by processing of the proteomic data via digestion of coronasformed on the surface of particles. Examples of proteins that can beidentified and measured include highly abundant proteins, proteins ofmedium abundance, and low-abundance proteins. A low abundance proteinmay be present in a sample at concentrations at or below about 10 ng/mL.A high abundance protein may be present in a sample at concentrations ator above about 10 μg/mL. A protein of moderate abundance may be presentin a sample at concentrations between about 10 ng/mL and about 10 μg/mL.Examples of proteins that are highly abundant proteins include albumin,IgG, and the top 14 proteins in abundance that contribute 95% of themass in plasma. Additionally, any proteins that may be purified using aconventional depletion column may be directly detected in a sample usingthe particle panels disclosed herein. Examples of proteins may be anyprotein listed in published databases such as Keshishian et al. (MolCell Proteomics. 2015 September; 14(9):2375-93. doi:10.1074/mcp.M114.046813. Epub 2015 Feb. 27.), Farr et al. (J ProteomeRes. 2014 Jan. 3; 13(1):60-75. doi: 10.1021/pr4010037. Epub 2013 Dec.6.), or Pememalm et al. (Expert Rev Proteomics. 2014 August;11(4):431-48. doi: 10.1586/14789450.2014.901157. Epub 2014 Mar. 24.).

In some embodiments, examples of proteins that can be measured andidentified using the particle panels disclosed herein include albumin,IgG, lysozyme, CEA, HER-2/neu, bladder tumor antigen, thyroglobulin,alpha-fetoprotein, PSA, CA 125, CA 19.9, CA 15.3, leptin, prolactin,osteopontin, IGF-II, CD98, fascin, sPigR, 14-3-3 eta, troponin I, B-typenatriuretic peptide, BRCA1, c-Myc, IL-6, fibrinogen. EGFR, gastrin, PH,G-CSF, desmin. NSE, FSH, VEGF, P21, PCNA, calcitonin, PR, CA 125, LH,somatostatin. S100, insulin. alpha-prolactin, ACTH, Bcl-2, ER alpha,Ki-67, p53, cathepsin D, beta catenin. VWF, CD15, k-ras, caspase 3, EPN,CD10, FAS, BRCA2. CD30L, CD30, CGA, CRP, prothrombin, CD44, APEX,transferrin, GM-CSF, E-cadherin, IL-2, Bax, IFN-gamma, beta-2-MG, TNFalpha, c-erbB-2, trypsin, cyclin D1, MG B, XBP-1, HG-1, YKL-40, S-gamma,NESP-55, netrin-1, geminin, GADD45A, CDK-6, CCL21, BrMS1, 17betaHDI,PDGFRA, Pcaf, CCL5, MMP3, claudin-4, and claudin-3. In some embodiments,other examples of proteins that can be measured and identified using theparticle panels disclosed herein are any proteins or protein groupslisted in the open targets database for a particular disease indicationof interest (e.g., prostate cancer, lung cancer, or Alzheimer'sdisease).

Methods of Analysis

The proteomic data of the sample can be identified, measured, andquantified using a number of different analytical techniques. Forexample, proteomic data can be analyzed using SDS-PAGE or any gel-basedseparation technique. Peptides and proteins can also be identified,measured, and quantified using an immunoassay, such as ELISA.Alternatively, proteomic data can be identified, measured, andquantified using mass spectrometry, high performance liquidchromatography, LC-MS/MS, Edman Degradation, immunoaffinity techniques,methods disclosed in EP3548652, WO2019083856, WO2019133892, each ofwhich is incorporated herein by reference in its entirety, and otherprotein separation techniques.

Computer Control Systems

The present disclosure provides computer control systems that areprogrammed to implement methods of the disclosure. This determination,analysis or statistical classification is done by methods known in theart, including, but not limited to, for example, a wide variety ofsupervised and unsupervised data analysis and clustering approaches suchas hierarchical cluster analysis (HCA), principal component analysis(PCA), Partial least squares Discriminant Analysis (PLSDA), machinelearning (also known as random forest), logistic regression, decisiontrees, support vector machine (SVM), k-nearest neighbors, naive bayes,linear regression, polynomial regression, SVM for regression, K-meansclustering, and hidden Markov models, among others. The computer systemcan perform various aspects of analyzing the protein sets or proteincorona of the present disclosure, such as, for example,comparing/analyzing the biomolecule corona of several samples todetermine with statistical significance what patterns are common betweenthe individual biomolecule coronas to determine a protein set that isassociated with the biological state. The computer system can be used todevelop classifiers to detect and discriminate different protein sets orprotein corona (e.g., characteristic of the composition of a proteincorona). Data collected from the presently disclosed sensor array can beused to train a machine learning algorithm, specifically an algorithmthat receives array measurements from a patient and outputs specificbiomolecule corona compositions from each patient. Before training thealgorithm, raw data from the array can be first denoised to reducevariability in individual variables.

Machine learning can be generalized as the ability of a learning machineto perform accurately on new, unseen examples/tasks after havingexperienced a learning data set. Machine learning may include thefollowing concepts and methods. Supervised learning concepts may includeAODE; Artificial neural network, such as Backpropagation, Autoencoders,Hopfield networks, Boltzmann machines, Restricted Boltzmann Machines,and Spiking neural networks; Bayesian statistics, such as Bayesiannetwork and Bayesian knowledge base; Case-based reasoning; Gaussianprocess regression; Gene expression programming; Group method of datahandling (GMDH); Inductive logic programming; Instance-based learning;Lazy learning; Learning Automata; Learning Vector Quantization; LogisticModel Tree; Minimum message length (decision trees, decision graphs,etc.), such as Nearest Neighbor Algorithm and Analogical modeling;Probably approximately correct learning (PAC) learning; Ripple downrules, a knowledge acquisition methodology; Symbolic machine learningalgorithms; Support vector machines; Random Forests; Ensembles ofclassifiers, such as Bootstrap aggregating (bagging) and Boosting(meta-algorithm); Ordinal classification; Information fuzzy networks(IFN); Conditional Random Field; ANOVA; Linear classifiers, such asFisher's linear discriminant, Linear regression, Logistic regression,Multinomial logistic regression, Naive Bayes classifier, Perceptron,Support vector machines; Quadratic classifiers; k-nearest neighbor;Boosting; Decision trees, such as C4.5, Random forests, ID3, CART, SLIQSPRINT; Bayesian networks, such as Naive Bayes; and Hidden Markovmodels. Unsupervised learning concepts may include;Expectation-maximization algorithm; Vector Quantization; Generativetopographic map; Information bottleneck method; Artificial neuralnetwork, such as Self-organizing map; Association rule learning, suchas, Apriori algorithm, Eclat algorithm, and FPgrowth algorithm;Hierarchical clustering, such as Singlelinkage clustering and Conceptualclustering; Cluster analysis, such as, K-means algorithm, Fuzzyclustering, DBSCAN, and OPTICS algorithm; and Outlier Detection, such asLocal Outlier Factor. Semi-supervised learning concepts may include;Generative models; Low-density separation; Graph-based methods; andCo-training. Reinforcement learning concepts may include; Temporaldifference learning; Q-learning; Learning Automata; and SARSA. Deeplearning concepts may include; Deep belief networks; Deep Boltzmannmachines; Deep Convolutional neural networks; Deep Recurrent neuralnetworks; and Hierarchical temporal memory. A computer system may beadapted to implement a method described herein. The system includes acentral computer server that is programmed to implement the methodsdescribed herein. The server includes a central processing unit (CPU,also “processor”) which can be a single core processor, a multi coreprocessor, or plurality of processors for parallel processing. Theserver also includes memory (e.g., random access memory, read-onlymemory, flash memory); electronic storage unit (e.g. hard disk);communications interface (e.g., network adaptor) for communicating withone or more other systems; and peripheral devices which may includecache, other memory, data storage, and/or electronic display adaptors.The memory, storage unit, interface, and peripheral devices are incommunication with the processor through a communications bus (solidlines), such as a motherboard. The storage unit can be a data storageunit for storing data. The server is operatively coupled to a computernetwork (“network”) with the aid of the communications interface. Thenetwork can be the Internet, an intranet and/or an extranet, an intranetand/or extranet that is in communication with the Internet, atelecommunication or data network. The network in some cases, with theaid of the server, can implement a peer-to-peer network, which mayenable devices coupled to the server to behave as a client or a server.

The storage unit can store files, such as subject reports, and/orcommunications with the data about individuals, or any aspect of dataassociated with the present disclosure.

The computer server can communicate with one or more remote computersystems through the network. The one or more remote computer systems maybe, for example, personal computers, laptops, tablets, telephones, Smartphones, or personal digital assistants.

In some applications the computer system includes a single server. Inother situations, the system includes multiple servers in communicationwith one another through an intranet, extranet and/or the internet.

The server can be adapted to store measurement data or a database asprovided herein, patient information from the subject, such as, forexample, medical history, family history, demographic data and/or otherclinical or personal information of potential relevance to a particularapplication. Such information can be stored on the storage unit or theserver and such data can be transmitted through a network.

Methods as described herein can be implemented by way of machine (orcomputer processor) executable code (or software) stored on anelectronic storage location of the server, such as, for example, on thememory, or electronic storage unit. During use, the code can be executedby the processor. In some cases, the code can be retrieved from thestorage unit and stored on the memory for ready access by the processor.In some situations, the electronic storage unit can be precluded, andmachine-executable instructions are stored on memory. Alternatively, thecode can be executed on a second computer system.

Aspects of the systems and methods provided herein, such as the server,can be embodied in programming. Various aspects of the technology may bethought of as “products” or “articles of manufacture” typically in theform of machine (or processor) executable code and/or associated datathat is carried on or embodied in a type of machine readable medium.Machine-executable code can be stored on an electronic storage unit,such memory (e.g., read-only memory, random-access memory, flash memory)or a hard disk. “Storage” type media can include any or all of thetangible memory of the computers, processors or the like, or associatedmodules thereof, such as various semiconductor memories, tape drives,disk drives and the like, which may provide non-transitory storage atany time for the software programming. All or portions of the softwaremay at times be communicated through the Internet or various othertelecommunication networks. Such communications, for example, may enableloading of the software from one computer or processor into another, forexample, from a management server or host computer into the computerplatform of an application server. Thus, another type of media that maybear the software elements includes optical, electrical, andelectromagnetic waves, such as used across physical interfaces betweenlocal devices, through wired and optical landline networks and overvarious air-links. The physical elements that carry such waves, such aswired or wireless likes, optical links, or the like, also may beconsidered as media bearing the software. As used herein, unlessrestricted to non-transitory, tangible “storage” media, terms such ascomputer or machine “readable medium” can refer to any medium thatparticipates in providing instructions to a processor for execution.

The computer systems described herein may comprise computer-executablecode for performing any of the algorithms or algorithms-based methodsdescribed herein. In some applications the algorithms described hereinwill make use of a memory unit that is comprised of at least onedatabase.

Data relating to the present disclosure can be transmitted over anetwork or connections for reception and/or review by a receiver. Thereceiver can be but is not limited to the subject to whom the reportpertains; or to a caregiver thereof, e.g., a health care provider,manager, other health care professional, or other caretaker; a person orentity that performed and/or ordered the analysis. The receiver can alsobe a local or remote system for storing such reports (e.g. servers orother systems of a “cloud computing” architecture). In one embodiment, acomputer-readable medium includes a medium suitable for transmission ofa result of an analysis of a biological sample using the methodsdescribed herein.

Aspects of the systems and methods provided herein can be embodied inprogramming. Various aspects of the technology may be thought of as“products” or “articles of manufacture” typically in the form of machine(or processor) executable code and/or associated data that is carried onor embodied in a type of machine readable medium. Machine executablecode can be stored on an electronic storage unit, such as memory (e.g.,read-only memory, random-access memory, flash memory) or a hard disk.“Storage” type media can include any or all of the tangible memory ofthe computers, processors or the like, or associated modules thereof,such as various semiconductor memories, tape drives, disk drives and thelike, which may provide nontransitory storage at any time for thesoftware programming. All or portions of the software may at times becommunicated through the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, froma management server or host computer into the computer platform of anapplication server. Thus, another type of media that may bear thesoftware elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links or the like, also may be considered as mediabearing the software. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

Hence, a machine readable medium, such as computer-executable code, maytake many forms, including but not limited to, a tangible storagemedium, a carrier wave medium or physical transmission medium.Non-volatile storage media include, for example, optical or magneticdisks, such as any of the storage devices in any computer(s) or thelike, such as may be used to implement the databases, etc. shown in thedrawings. Volatile storage media include dynamic memory, such as mainmemory of such a computer platform. Tangible transmission media includecoaxial cables; copper wire and fiber optics, including the wires thatcomprise a bus within a computer system. Carrier-wave transmission mediamay take the form of electric or electromagnetic signals, or acoustic orlight waves such as those generated during radio frequency (RF) andinfrared (IR) data communications. Common forms of computer-readablemedia therefore include for example: a floppy disk, a flexible disk,hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD orDVD-ROM, any other optical medium, punch cards paper tape, any otherphysical storage medium with patterns of holes, a RAM, a ROM, a PROM andEPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer may readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying one or more sequences of one or moreinstructions to a processor for execution.

Classification of Protein Corona Using Machine Learning

The method of determining a set of proteins associated with the diseaseor disorder and/or disease state include the analysis of the corona ofthe at least two samples. This determination, analysis or statisticalclassification is done by methods known in the art, including, but notlimited to, for example, a wide variety of supervised and unsuperviseddata analysis, machine learning, deep learning, and clusteringapproaches including hierarchical cluster analysis (HCA), principalcomponent analysis (PCA), Partial least squares Discriminant Analysis(PLS-DA), random forest, logistic regression, decision trees, supportvector machine (SVM), k-nearest neighbors, naive bayes, linearregression, polynomial regression, SVM for regression, K-meansclustering, and hidden Markov models, among others. In other words, theproteins in the corona of each sample are compared/analyzed with eachother to determine with statistical significance what patterns arecommon between the individual corona to determine a set of proteins thatis associated with the disease or disorder or disease state.

Generally, machine learning algorithms are used to construct models thataccurately assign class labels to examples based on the input featuresthat describe the example. In some case it may be advantageous to employmachine learning and/or deep learning approaches for the methodsdescribed herein. For example, machine learning can be used to associatethe protein corona with various disease states (e.g. no disease,precursor to a disease, having early or late stage of the disease,etc.). For example, in some cases, one or more machine learningalgorithms are employed in connection with a method of the invention toanalyze data detected and obtained by the protein corona and sets ofproteins derived therefrom. For example, in one embodiment, machinelearning can be coupled with the sensor array described herein todetermine not only if a subject has a pre-stage of cancer, cancer ordoes not have or develop cancer, but also to distinguish the type ofcancer.

NUMBERED EMBODIMENTS

The following embodiments recite non-limiting permutations ofcombinations of features disclosed herein. Other permutations ofcombinations of features are also contemplated. In particular, each ofthese numbered embodiments is contemplated as depending from or relatingto every previous or subsequent numbered embodiment, independent oftheir order as listed. 1. A method of identifying proteins in a sample,the method comprising: incubating a panel comprising a plurality ofparticle types with the sample to form a plurality of protein corona;digesting the plurality of protein coronas to generate proteomic data;and identifying proteins in the sample by quantifying the proteomicdata. 2. The method of embodiment 1, wherein the sample is from asubject. 3. The method of embodiment 2, wherein the method furthercomprises determining a protein profile of the sample from theidentifying step and associating the protein profile with a biologicalstate of the subject. 4. The method of embodiment 1, wherein the methodfurther comprises determining a biological state of the sample from thesubject by: generating proteomic data by digesting the plurality ofprotein coronas; determining a protein profile of the plurality ofprotein coronas; and associating the protein profile with the biologicalstate, wherein the panel comprises at least two different particletypes. 5. The method of embodiment 4, wherein the associating isperformed by a trained classifier. 6. The method of any one ofembodiments 1-5, wherein the panel comprises at least three differentparticle types, at least four different particle types, at least fivedifferent particle types, at least six different particle types, atleast seven different particle types, at least eight different particletypes, at least nine different particle types, at least 10 differentparticle types, at least 11 different particle types, at least 12different particle types, at least 13 different particle types, at least14 different particle types, at least 15 different particles, or atleast 20 different particle types. 7. The method of any one ofembodiments 1-6, wherein the panel comprises at least four differentparticle types. 8. The method of any one of embodiments 1-7, wherein atleast one particle type of the panel comprises a physical feature thatis different from a second particle type of the panel. 9. The method ofembodiment 8, wherein the physical feature is size, polydispersityindex, surface charge, or morphology. 10. The method of any one ofembodiments 1-9, wherein the size of at least one particle type of theplurality of particle types in the panel is from 10 nm to 500 nm. 11.The method of any one of embodiments 1-10, wherein the polydispersityindex of at least one particle type of the plurality of particle typesin the panel is from 0.01 to 0.25. 12. The method of any one ofembodiments 1-11, wherein the morphology of at least one particle typeof the plurality of particle types comprises spherical, colloidal,square shaped, rods, wires, cones, pyramids, or oblong. 13. The methodof embodiment 1-12, wherein the surface charge of at least one particletype of the plurality of particle types comprises a positive surfacecharge. 14. The method of any one of embodiments 1-12, wherein thesurface charge of at least one particle type of the plurality ofparticle types comprises a negative surface charge. 15. The method ofany one of embodiments 1-12, wherein the surface charge of at least oneparticle type of the plurality of particle types comprises a neutralsurface charge. 16. The method of any one of embodiments 1-15, whereinat least one particle type of the plurality of particle types comprisesa chemical feature that is different from a second particle type of thepanel. 17. The method of embodiment 16, wherein the chemical feature isa surface functional chemical group. 18. The method of embodiment 17,wherein the functional chemical group is an amine or a carboxylate. 19.The method of any one of embodiments 1-18, wherein at least one particletype of the plurality of particle types is made of a material comprisinga polymer, a lipid, or a metal, silica, a protein, a nucleic acid, asmall molecule, or a large molecule. 20. The method of embodiment 19,wherein the polymer comprises polyethylenes, polycarbonates,polyanhydrides, polyhydroxyacids, polypropylfumerates,polycaprolactones, polyamides, polyacetals, polyethers, polyesters,poly(orthoesters), polycyanoacrylates, polyvinyl alcohols,polyurethanes, polyphosphazenes, polyacrylates, polymethacrylates,polycyanoacrylates, polyureas, polystyrenes, or polyamines, apolyalkylene glycol (e.g., polyethylene glycol (PEG)), a polyester(e.g., poly(lactide-co-glycolide) (PLGA), polylactic acid, orpolycaprolactone), polystyrene, or a copolymer of two or more polymers.21. The method of embodiment 19, wherein the lipid comprisesdioleoylphosphatidylglycerol (DOPG), diacylphosphatidylcholine,diacylphosphatidylethanolamine, ceramide, sphingomyelin, cephalin,cholesterol, cerebrosides and diacylglycerols,dioleoylphosphatidylcholine (DOPC), dimyristoylphosphatidylcholine(DMPC), and dioleoylphosphatidylserine (DOPS), phosphatidylglycerol,cardiolipin, diacylphosphatidylserine, diacylphosphatidic acid,N-dodecanoyl phosphatidylethanolamines, N-succinylphosphatidylethanolamines, N-glutarylphosphatidylethanolamines,lysylphosphatidylglycerols, palmitoyloleyolphosphatidylglycerol (POPG),lecithin, lysolecithin, phosphatidylethanolamine,lysophosphatidylethanolamine, dioleoylphosphatidylethanolamine (DOPE),dipalmitoyl phosphatidyl ethanolamine (DPPE),dimyristoylphosphoethanolamine (DMPE),distearoyl-phosphatidyl-ethanolamine (DSPE),palmitoyloleoyl-phosphatidylethanolamine (POPE)palmitoyloleoylphosphatidylcholine (POPC), egg phosphatidylcholine(EPC), distearoylphosphatidylcholine (DSPC), dioleoylphosphatidylcholine(DOPC), dipalmitoylphosphatidylcholine (DPPC),dioleoylphosphatidylglycerol (DOPG), dipalmitoylphosphatidylglycerol(DPPG), palmitoyloleyolphosphatidylglycerol (POPG), 16-O-monomethyl PE,16-O-dimethyl PE, 18-1-trans PE,palmitoyloleoyl-phosphatidylethanolamine (POPE),1-stearoyl-2-oleoyl-phosphatidyethanolamine (SOPE), phosphatidylserine,phosphatidylinositol, sphingomyelin, cephalin, cardiolipin, phosphatidicacid, cerebrosides, dicetylphosphate, or cholesterol. 22. The method ofembodiment 19, wherein the metal comprises gold, silver, copper, nickel,cobalt, palladium, platinum, iridium, osmium, rhodium, ruthenium,rhenium, vanadium, chromium, manganese, niobium, molybdenum, tungsten,tantalum, iron, or cadmium, titanium, or gold. 23. The method of any oneof embodiments 1-22, wherein at least one particle type of the pluralityof particle types is surface functionalized with a polymer comprisingpolyethylenes, polycarbonates, polyanhydrides, polyhydroxyacids,polypropylfumerates, polycaprolactones, polyamides, polyacetals,polyethers, polyesters, poly(orthoesters), polycyanoacrylates, polyvinylalcohols, polyurethanes, polyphosphazenes, polyacrylates,polymethacrylates, polycyanoacrylates, polyureas, polystyrenes, orpolyamines, a polyalkylene glycol (e.g., polyethylene glycol (PEG)), apolyester (e.g., poly(lactide-co-glycolide) (PLGA), polylactic acid, orpolycaprolactone), polystyrene, or a copolymer of two or more polymerspolyethylene glycol. 24. The method of any one of embodiments 3-23,wherein the method associates the protein profile to the biologicalstate with at least 70% accuracy, at least 75% accuracy, at least 80%accuracy, at least 85% accuracy, at least 90% accuracy, at least 92%accuracy, at least 95% accuracy, at least 96% accuracy, at least 97%accuracy, at least 98% accuracy, at least 99% accuracy, or 100%accuracy. 25. The method of any one of embodiments 3-24, wherein themethod associates the protein profile to the biological state with atleast 70% sensitivity, at least 75% sensitivity, at least 80%sensitivity, at least 85% sensitivity, at least 90% sensitivity, atleast 92% sensitivity, at least 95% sensitivity, at least 96%sensitivity, at least 97% sensitivity, at least 98% sensitivity, atleast 99% sensitivity, or 100% sensitivity. 26. The method of any one ofembodiments 3-25, wherein the method associates the protein profile tothe biological state with at least 70% specificity, at least 75%specificity, at least 80% specificity, at least 85% specificity, atleast 90% specificity, at least 92% specificity, at least 95%specificity, at least 96% specificity, at least 97% specificity, atleast 98% specificity, at least 99% specificity, or 100% specificity.27. The method of any one of embodiments 1-26, wherein the methodidentifies at least 100 unique proteins, at least 200 unique proteins,at least 300 unique proteins, at least 400 unique proteins, at least 500unique proteins, at least 600 unique proteins, at least 700 uniqueproteins, at least 800 unique proteins, at least 900 unique proteins, atleast 1000 unique proteins, at least 1100 unique proteins, at least 1200unique proteins, at least 1300 unique proteins, at least 1400 uniqueproteins, at least 1500 unique proteins, at least 1600 unique proteins,at least 1700 unique proteins, at least 1800 unique proteins, at least1900 unique proteins, or at least 2000 unique proteins. 28. The methodof any one of embodiments 1-27, wherein at least one particle type ofthe plurality of particle types comprises a superparamagnetic iron oxidenanoparticle. 29. The method of any one of embodiments 1-28, wherein thesample is a biofluid. 30. The method of embodiment 29, wherein thebiofluid comprises plasma, serum, CSF, urine, tear, or saliva 31. Amethod of selecting a panel for protein corona analysis, comprisingselecting a plurality of particle types with at least three differentphysicochemical properties. 32. The method of embodiment 31, wherein thedifferent physicochemical properties is selected from a group consistingof surface charge, surface chemistry, size, and morphology. 33. Themethod of embodiment 32, wherein the different physicochemicalproperties comprises surface charge. 34. A composition comprising apanel of particles, wherein the panel comprises a plurality of particletypes and wherein the plurality of particle types comprises at leastthree different physicochemical properties. 35. The composition ofembodiment 35, wherein the different physicochemical properties isselected from a group consisting of surface charge, surface chemistry,size, and morphology. 36. The composition of embodiment 36, wherein thedifferent physicochemical properties comprises surface charge. 37. Asystem of comprising the panel of any one of embodiments 1-34. 38. Asystem comprising a panel, wherein the panel comprises a plurality ofparticle types. 39. The system of embodiment 38, wherein the pluralityof particle types comprises at least three different physicochemicalproperties. 40. The system of embodiment 38, wherein the panel comprisesat least 3, at least 4, at least 5, at least 6, at least 7, at least 8,at least 9, at least 10, at least 11, or at least 12 different particletypes. 41. The system of embodiment 38, wherein the plurality ofparticle types are capable of adsorbing a plurality of proteins from asample to form a plurality of protein coronas. 42. The system ofembodiment 41, wherein the plurality of protein coronas are digested todetermine a protein profile. 43. The system of embodiment 42, whereinthe protein profile is associated with a biological state using atrained classifier.

EXAMPLES

The following examples are included to further describe some aspects ofthe present disclosure, and should not be used to limit the scope of theinvention.

Example 1 Synthesis of SPMNPs or Superparamagnetic Iron OxideNanoparticles (SPIONs)

SPMNPs or SPIONs were synthesized by following the method in theliterature (Angew. Chem. Int. Ed. 2009, 48, 5875-5879 & Langmuir 2012,28, 3271-3278) via solvothermal reaction at 200° C. by reduction ofFeCl3 with ethylene glycol (EG) in the presence of sodium acetate(NaOAc) as an alkali source and trisodium citrate (Na₃Cit) as anelectrostatic stabilizer. The excess EG acted as both the solvent andreductant.

Starting materials: Iron (III) chloride hexahydrate (FeCl₃.6H₂O), MW270.30, CAS #10025-77-1; Sodium acetate (NaOAc), MW 82.03, CAS#127-09-3; Trisodium citrate dihydrate (Na₃Cit 2H₂0), MW 294.10, CA#6132-04-3; Ethylene glycol (EG), MW 62.07, CAS #107-21-1.

Procedure for Fe₃O₄ Nanoparticles Via Solvothermal Reaction:

(1) Typically, FeCl₃ (0.65 g, 4.0 mmol) and sodium citrate (0.20 g, 0.68mmol) were first dissolved in EG (20 mL), afterward, sodium acetate(1.20 g, 14.6 mmol) was added with stirring. The mixture was stirredvigorously for 30 min at 160° C. and then sealed in a Teflon-linedstainless-steel autoclave (50 mL capacity). (2) The autoclave was heatedat 200° C. and maintained for 12 h, and then allowed to cool to roomtemperature. (3) The black products were isolated by a magnet and washedwith DI water for >5 times. (4) The final Fe₃O₄ nanoparticle product wasdried in vacuum at 60° C. for 12 h or freeze dried to a black powder.Fe₃O₄@SiO₂ core/shell colloids were prepared through a modified Stoberprocess by following the method in the literature (J. Am. Chem. Soc.2008, 130, 28-29 & J. Mater. Chem. B, 2013, 1, 4684-4691)

Starting materials: Fe₃O₄ nanoparticles synthesized via the solvothermalreaction; Tetraethyl orthosilicate (TEOS), MW 208.33, CAS #78-10-4;Ammonia solution 25%; Cetrimonium bromide (CTAB), MW 364.45, CAS#57-09-0; (3-Aminopropyl)triethoxysilane (APTES), MW 221.37, CAS#919-30-2.

Procedure for Fe₃O₄@SiO₂ Core/Shell Nanoparticles:

(1) Superparamagnetic Fe₃O₄ NPs were synthesized according to thepreviously reported solvothermal reaction (Angew. Chem. Int. Ed. 2009,48, 5875-5879 & Langmuir 2012, 28, 3271-3278). Then the obtained Fe₃O₄nanoparticles were used to prepare highly aminated superparamagneticmesoporous composite nanoparticles through a two-step coating procedure.

(2) In brief, 0.08 g of the Fe₃O₄ nanoparticles were homogeneouslydispersed in the mixture of ethanol (50 mL), DI water (1 mL), andconcentrated ammonia aqueous solution (1.7 mL, 25 wt %), followed by theaddition of TEOS (140 μL). After stirring at 40° C. for 6 h, amorphoussilica coated superparamagnetic nanoparticles (denoted as Fe₃O₄@SiO₂)were obtained and washed 5 times with water. (3) Then, the Fe₃O₄@SiO₂nanoparticles were coated with highly aminated mesoporous silica shellvia base-catalyzed sol-gel silica reactions by using CTAB as a template.Typically, the above prepared the Fe₃O₄@SiO₂ nanoparticles (5 mg) weredispersed in a mixed solution containing CTAB (0.08 g), ethyl acetate(0.7 mL), DI water (113 mL), and concentrated ammonia (2.42 mL, 25 wt%). TEOS (0.18 mL) and APTES (0.22 mL) were added to the dispersion witha stirring speed of 300 rpm. After reaction at room temperature for 3 h,the products were collected with a magnet and washed repeatedly withwater and ethanol, respectively.

(4) To remove the pore-generating template (CTAB), the as-synthesizedmaterials were transferred to ethanol (60 mL) with continual stirring at60° C. for 3 h. The surfactant extraction step was repeated two times toensure the removal of CTAB. The template-removed products were washedwith ethanol twice, resulting in sandwich structured highly aminatedsuperparamagnetic mesoporous composite nanoparticles(Fe₃O₄@SiO₂@mSiO₂—NH₂).

Fe₃O₄@polymer composite nanoparticles were synthesized throughsurfactant-free seeded emulsion polymerization (Langmuir 2012, 28,3271-3278).

Fe₃O₄ nanoparticles synthesized via the solvothermal reaction.

Starting materials: Fe₃O₄ nanoparticles synthesized via the solvothermalreaction; Tetraethyl orthosilicate (TEOS), MW 208.33, CAS #78-10-4;3-(trimethoxysilyl)propyl methacrylate (MPS), MW 248.35, CAS #2530-85-0;Ammonia solution 25%; Divinylbenzene (DVB), MW 130.19, CAS #1321-74-0;Styrene (St), MW 104.15, CAS #100-42-5; Methacrylic acid (MAA), MW86.09, CAS #79-41-4; Ammonium persulfate (APS), MW 228.20, CAS#7727-54-0.

Procedure for Fe₃O₄@Polymer Core-Shell Nanoparticles:

(1) SPIONs were synthesized according to the previously reportedsolvothermal reaction (Angew. Chem. Int. Ed. 2009, 48, 5875-5879 &Langmuir 2012, 28, 3271-3278). (2) Fe₃O₄@MPS were prepared through amodified Stober process. Typically, 1 g of Fe₃O₄ nanoparticles werehomogeneously dispersed in the mixture of ethanol (50 mL), DI water (2mL), and concentrated ammonia aqueous solution (2 mL, 25 wt %), followedby the addition of TEOS (200 μL) and MPS (2 mL). After stirring at 70°C. for 24 h, the MPS coated superparamagnetic NPs were obtained andwashed 5 times with water and freeze dried to a dark brown powder andstored at −20° C. (3) Fe₃O₄@Polymer nanoparticles were synthesizedthrough surfactant-free seeded emulsion polymerization. Typically, 100mg of Fe₃O₄@MPS were homogeneously dispersed in 125 mL of DI water.After bubbling with N₂ for 30 min, 2 mL of St, 0.2 mL of DVB and 0.4 mLof MAA were added into the Fe₃O₄@MPS suspension. After addition of 5 mLof 0.2 g of NaOH and 40 mg of APS aqueous solution, the resultingmixture was heated to 75° C. overnight. (4) After cooling down,Fe₃O₄@P(St-co-MAA) were obtained and washed 5 times with water andfreeze dried to a dark brown powder.

Example 2 Building Panels of Particle Types

This example describes building panels of particle types. A particlelibrary was built comprising ˜170 total particle types. Biomoleculecoronas were produced for each particle type, by incubating eachparticle with a biological sample. Proteomic data from protein coronaswere analyzed for each particle type, including qualitative analysisbased on electrophoresis and quantitative analysis based on massspectrometry data and heat maps. Panels were built by choosing certainparticle types based on particle and corona properties. Particle typeswere selected for particle panels based on the broadness of coverage aparticle type had for proteins in the plasma sample. FIG. 5 illustratesthe process for generating proteomic data and the process for panelselection. As shown in FIG. 6, particle properties includesize/geometry, charge, surface functionality, magnetism, in addition toother properties. Each of these properties can be assays by a variety oftests in fully characterizing a particle type before adding it to apanel. As shown in FIG. 7 dynamic light scattering was used tocharacterize the size distribution of two particle types: SP-002(phenol-formaldehyde coated particles) and SP-010 (carboxylate, PAAcoated particles). As shown in FIG. 8, TEM was used to characterize thesize and morphology of two particle types including SP-002(phenol-formaldehyde coated particles) (FIG. 8A) and SP-339 (polystyrenecarboxyl particles) (FIG. 8B). As shown in FIG. 9, XPS can be used toanalyze chemical groups present at the surface of various particle typesincluding SP-333 (carboxylate), SP-339 (polystyrene carboxylate), SP-356(silica amino), SP-374 (silica silanol), HX-20 (silica coated), HX-42(silica coated, amine), and HX-74 (PDMPAPMA coated (dimethylamine).

Example 3 Synthesis and Characterization of Iron Oxide NPs with DistinctSurface Chemistries

This example describes synthesis and characterization of iron oxide NPswith distinct surface chemistries. To address the need for robustparticles that can be easily separated, without the need for, but whichis also capable of withstanding, repeated centrifugation or membranefiltration to separate particle protein corona from free plasma proteinsand to wash away loosely attached proteins from the particles,superparamagnetic iron oxide NPs (SPIONs) were developed (FIG. 23, attop) for protein corona formation. The iron oxide particle corefacilitated rapid separation of the particles from plasma solution in<30 sec using a magnet (FIG. 4). This drastically reduced the timeneeded for extraction of particle protein corona for LC-MS/MS analysis.Moreover, SPIONs were robustly modified with different surfacechemistries, which facilitated the generation of distinct patterns ofprotein corona for more broadly interrogating the proteome.

Three SPIONs (SP-003, SP-007, and SP-011) with different surfacefunctionalization were synthesized (FIG. 28). SP-003 was coated with athin layer of silica by a modified Stober process using tetraethylorthosilicate (TEOS). For synthesis of poly(dimethyl aminopropylmethacrylamide) (PDMAPMA)-coated SPIONs (SP-007) and poly(ethyleneglycol) (PEG)-coated SPIONs (SP-011), w the iron oxide particle core wasfirst modified with vinyl groups by a modified Stober process using TEOSand 3-(trimethoxysilyl)propyl methacrylate. Next, the vinylgroup-functionalized SPIONs were surface modified by free radicalpolymerization with N-[3-(dimethylamino)propyl] methacrylamide andpoly(ethylene glycol) methyl ether methacrylate, respectively, toprepare SP-007 and SP-011.

The three SPIONs were characterized using various techniques, includingscanning electron microscopy (SEM), dynamic light scattering (DLS),transmission electron microscopy (TEM), high-resolution TEM (HRTEM), andX-ray photoelectron spectroscopy (XPS), to evaluate the size,morphology, and surface properties of SPIONs (FIG. 23). The results ofDLS measurements showed that SP-003, SP-007, and SP-011 had averagesizes of ˜233 nm, ˜283 nm, and ˜238 nm, respectively. This wasconsistent with SEM measurements, which showed that all three SPIONs hadspherical and semi-spherical morphologies with sizes ranging from 200 nmto 300 nm. The surface charge of SPIONs was evaluated by zeta potential(ζ) analysis, which showed ζ-potential values of −36.9 mV, +25.8 mV, and−0.4 mV for SP-003, SP-007, and SP-011, respectively, at pH 7.4 (TABLE2-4).

TABLE 2 Particle diameter and zeta potential of SP-003 SPION, asmeasured by DLS Z-average Zeta potential Measurement # size PDI (mV) 1233.8 0.053 −36.4 2 235.3 0.039 −36.8 3 230.4 0.055 −37.4 Average 233.20.05  −36.9 nm mV

TABLE 3 Particle diameter and zeta potential of SP-007 SPION, asmeasured by DLS Z-average Zeta potential Measurement # size PDI (mV) 1284.4 0.049 25.7 2 286.1 0.119 25.9 3 279.7 0.113 25.9 Average 283.40.09  +25.8 nm mV

TABLE 4 Particle diameter and zeta potential of SP-011 SPION, asmeasured by DLS Z-average Zeta potential Measurement # size PDI (mV) 1236.5 0.207 0.08 2 238.9 0.198 −0.67 3 237.6 0.201 −0.74 Average 237.70.2  −0.4 nm mV

This indicated that the SP-003, SP-007, and SP-011 had negative,positive, and neutral surfaces, which was consistent with the charge ofcoating functionalities used to modify the surface of each particle asshown in the schematics of FIG. 23. The thickness of the coatings wasevaluated using HRTEM. For SP-003, a complete amorphous shell wasobserved around the iron oxide core with a thickness greater than 10 nm(FIG. 23, column 5 at top). For SP-007 and SP-011, a relatively thin(<10 nm) amorphous feature was observed at the surface of particles(arrows in FIG. 23; column 5, at middle and bottom). In addition, XPSwas performed for surface analysis, which, along with HRTEM images,confirmed the successful coating of the particles with respectivefunctional groups.

Example 4 Detection of Proteins with a Panel and Association of ProteinProfiles with a Cancer

This example describes detection of proteins with a panel andassociation of protein profiles with a cancer. The panel of particlesincludes three different cross-reactive liposomes with various surfacecharges (anionic (DOPG(1,2-dioleoyl-sn-glycero-3-phospho-fl′-rac-glycerol))), cationic (DOTAP(1,2-Dioleoyl-3-trimethylammoniumpropane)-DOPE(dioleoylphosphatidylethanolamine)), and neutral(dioleoylphosphatidylcholine (DOPC) with cholesterol).

FIG. 17A shows a schematic of the processes of the present applicationincluding collection of samples from healthy and cancer patients,isolation of plasma from the samples, incubation with uncoated liposomesto form protein coronas, and enrichment of select plasma proteins.Protein corona formation can be different based on the physicochemicalproperties of particles. FIG. 17B shows corona analysis signals for athree-particle type panel. Plasma was collected from 45 subjects (8 fromeach of five cancers including glioblastoma, lung cancer, meningioma,myeloma, and pancreatic cancer and 5 healthy controls). Corona analyseswere created for each particle in the three-particle type panel. Randomforest models were built in each of 1000 rounds of cross-validation.There is strong evidence for robust corona analysis signal. Initialexploratory analysis was done via PCA. FIG. 18A and FIG. 18B show thatearly stage cancers can be separated up to 8 years before symptomsdevelop. The Golestan Cohort enrolled 50,000 healthy subjects between2004 and 2008. As shown in FIG. 18A, banked plasma from enrollment wastested. 8 years after enrollment, approximately 1000 patients developedcancers. FIG. 18B shows the classification of the banked plasma. Coronaanalysis of banked plasma from enrollment date accurately classifiedcancers for 15 out of 15 subjects examined (5 patients each for 3cancers). FIG. 19 shows the robust classification of five cancers usinga three-particle type corona analysis with an overall accuracy of 95%.The data shows that adding particle type diversity increasesperformance. Three different liposomes with negative, neutral, andpositive net charge on the surfaces (at pH 7.4) were used.

Example 5 Rapid and Deep Proteomic Analysis by the Corona AnalysisWorkflow

This example describes rapid and deep proteomic analysis by the coronaanalysis workflow. To evaluate the multi-particle type protein coronaanalysis platform (FIG. 22B) for analysis of plasma proteome, SPIONswere tested with a pooled plasma sample combined from eight colorectalcancer (CRC) cancer subjects. Each of these three particle types wasfirst incubated with the plasma sample for about 1 hour at about 37° C.for protein corona formation, followed by a magnet-based purification ofparticles from unbound proteins (6 min per cycle for 3 times). Theproteins bound onto particle were then lysed, digested, purified andeluted; these steps taking ˜2-4 hours combined, before MS analysis.Notably, this preparation workflow required only ˜4-6 hours in total fora batch of 96 corona samples.

After MS analysis and data processing, the resulting MS2peptide-spectral matches (PSM) were used to identify proteins present ineach particle type corona. In parallel, proteins were also detected froma neat plasma sample directly, without particle corona formation.Comparing the identified proteins from the samples to a compileddatabase of MS measured or inferred plasma protein concentrations, thedepth and extent of coverage by particle corona or plasma was examinedby plotting observed proteins versus the database values of publishedprotein concentrations (FIG. 24). First, the 1,255 proteins from thedatabase covering almost 11-orders of magnitude in order from mostabundant to least abundant protein were plotted. For each of theexperimentally evaluated samples (neat plasma vs. SP-003/SP-007/SP-011particle corona), the proteins matching the database were similarlyplotted. As can be seen in FIG. 24, the measured plasma proteome'sdynamic range as defined by the range of concentrations fordatabase-matching proteins was 2-fold greater for particle corona (e.g.,from 40 mg/mL to 0.54 ng/mL for SP-007) than it was for neat plasma(from 40 mg/mL to 1.2 ng/mL) with a 10-fold increase in the number oflow abundant proteins present below 100 ng/mL (842 for particles and 84for neat plasma). There were only 12 proteins annotated in the databasewith a lower concentration than the lowest protein detected on theparticles. In addition, the total number of unique proteins for each ofthe particle type corona (˜1,000) is greater (>2-fold) than thatobserved for neat plasma (<500), as clearly demonstrated in TABLE 5.

TABLE 5 Coverage of proteins identified by SP-003, SP-007, and SP-011particles versus neat plasma Group Total Proteins Match to DatabaseFraction in Database Plasma 492 272 0.55 SP-003 1062 387 0.36 SP-007 991383 0.39 SP-011 1062 393 0.37

In addition, the fraction of proteins that were previously unobserved bycomparison to the literature MS compilation was greater (61-64%) forparticles as compared to neat plasma (45%). In other words, moreproteins unannotated with a prior MS concentration in the publisheddatabase were identified in particle corona than were observed in neatplasma. The plot of the particle protein identifications which overlapthe database confirm that different particle types select differentialsubsets of the plasma proteins. This could be attributable to thedifferent surface properties of the three SPION particle types, whichlargely determine the protein composition of corona.

In order to evaluate the ability of particles to compress the measureddynamic range, measured and identified protein feature intensities werecompared to the published values for the concentration of the sameprotein. First, the resulting peptide features for each protein (aspresented in FIG. 24) was selected with the maximum MS-determinedintensity of all possible features for a protein (using the OpenMS MSdata processing tools to extract monoisotopic peak values), and then theintensities were modeled against the published abundance levels forthose same proteins (FIG. 25). By comparing the regression model slopesand the intensity span of the measured data, the particle coronascontain more proteins at lower abundances (measured or reported) thandoes plasma, similar to FIG. 24. The dynamic range of those measuredvalues was compressed (the slope of the regression model is reduced) forparticle measurements as compared to plasma measurements. This wasconsistent with previous observations that particle can effectivelycompress the measured dynamic range for abundances in the resultingcorona as compared to the original dynamic range in plasma, which couldbe attributable to the combination of absolute concentration of aprotein, its binding affinity to particles, and its interactions withneighboring proteins. All the above results indicate that themulti-particle type protein corona strategy facilitated theidentification of a broad spectrum of plasma proteins, particularlythose in the low abundance that are challenging for rapid detection byconventional proteomic techniques.

To evaluate the robustness of protein identification using the particlecorona MS assay, full-assay triplicates were performed using the threeparticle type panel to create individual protein corona samples from thesame pooled CRC plasma sample. For each combination of particle typesranging from any one, to all groups of two, to the single group ofthree, the number of unique proteins enumerated by the combination isshown in TABLE 6.

TABLE 6 Summary of the protein coverage from the combinations of SP-003,SP-007 and SP-011 Particles Particle Type Combination Only One Any OneAll Three SP-003 1058 ± 27.8 1313 844 SP-007  961 ± 87.4 1277 660 SP-0111022 ± 37.8 1249 821 SP-003:SP-007 1412 ± 18.9 1816 1052 SP-003:SP-0111272 ± 22.5 1595 973 SP-007:SP-011 1372 ± 27.3 1746 1026SP-003:SP-007:SP-011 1576 ± 14.5 2030 1150

In the ‘Only One’ column, the protein counts were developed using eachof the three replicates independently and then finding the mean andstandard deviation for all of the combination counts. As can be seen,more proteins were discovered when increasing the number of particletypes in the particle panel, with >1,500 unique proteins by the group ofthree particle types (65 of which are FDA-cleared/approved biomarkers,as listed in TABLE 7, below). In the ‘Any One’ replicate column, theprotein counts were developed using the union of a particle typereplicate protein lists. In the ‘All Three’ replicates column, theprotein counts were developed using the intersection of a particle typereplicate protein lists. As an additional measure of particle replicateoverlap of identified proteins, the Jaccard Index, a metric for setsimilarity, was calculated for each pairwise-comparison. The values forSP-003, SP-007, and SP-011 were 0.74 +0.018, 0.65 0.078, and 0.76±0.019(mean±sd), respectively. Enumeration of protein content in a given MSsample is subject to the stochastic nature of MS2 data collection andmay represent an undercount of the proteins represented within a sampleor shared in common between samples. PSM mapping to shared MS1 featuresrepresents one approach that may alleviate this issue and will bedeveloped for future analysis.

TABLE 7 FDA-Cleared/Approved Biomarkers UP_Accession UP_Name ClassP02647 APOA1_HUMAN Particles P00747 PLMN_HUMAN Particles P02671FIBA_HUMAN Particles P02675 FIBB_HUMAN Particles P04114 APOB_HUMANParticles P02775 CXCL7_HUMAN Particles P02768 ALBU_HUMAN ParticlesP02679 FIBG_HUMAN Particles P0C0L4 CO4A_HUMAN Particles P0C0L5CO4B_HUMAN Particles P61626 LYSC_HUMAN Particles P0DOY2 IGLC2_HUMANParticles P01024 CO3_HUMAN Particles P08519 APOA_HUMAN Particles P04075ALDOA_HUMAN Particles P00738 HPT_HUMAN Particles P00736 C1R_HUMANParticles P00488 F13A_HUMAN Particles P02765 FETUA_HUMAN ParticlesP01023 A2MG_HUMAN Particles P61769 B2MG_HUMAN Particles P01009A1AT_HUMAN Particles P01834 IGKC_HUMAN Particles P00751 CFAB_HUMANParticles P02746 C1QB_HUMAN Particles P07225 PROS_HUMAN Particles P02751FINC_HUMAN Particles P00450 CERU_HUMAN Particles P02747 C1QC_HUMANParticles P01031 CO5_HUMAN Particles P05155 IC1_HUMAN Particles P09871C1S_HUMAN Particles P02790 HEMO_HUMAN Particles P02745 C1QA_HUMANParticles P01034 CYTC_HUMAN Particles P08697 A2AP_HUMAN Particles P02741CRP_HUMAN Particles P17936 IBP3_HUMAN Particles P01008 ANT3_HUMANParticles P04278 SHBG_HUMAN Particles P19652 A1AG2_HUMAN ParticlesP02787 TRFE_HUMAN Particles P02786 TFR1_HUMAN Particles P02763A1AG1_HUMAN Particles P04275 VWF_HUMAN Particles P07195 LDHB_HUMANParticles P00338 LDHA_HUMAN Particles P30613 KPYR_HUMAN Particles P02766TTHY_HUMAN Particles P09972 ALDOC_HUMAN Particles O75874 IDHC_HUMANParticles P06858 LIPL_HUMAN Particles P05164 PERM_HUMAN Particles P05121PAI1_HUMAN Particles P00740 FA9_HUMAN Particles P05543 THBG_HUMANParticles P04070 PROC_HUMAN Particles P08833 IBP1_HUMAN Particles P00742FA10_HUMAN Particles P07477 TRY1_HUMAN Particles P07478 TRY2_HUMANParticles P02753 RET4_HUMAN Particles P43251 BTD_HUMAN Particles P24666PPAC_HUMAN Particles P05160 F13B_HUMAN Particles P01023 A2MG_HUMANPlasma P02768 ALBU_HUMAN Plasma P02671 FIBA_HUMAN Plasma P01008ANT3_HUMAN Plasma P01024 CO3_HUMAN Plasma P00450 CERU_HUMAN PlasmaP02775 CXCL7_HUMAN Plasma P02787 TRFE_HUMAN Plasma P08697 A2AP_HUMANPlasma P01031 CO5_HUMAN Plasma P0C0L4 CO4A_HUMAN Plasma P0C0L5CO4B_HUMAN Plasma P01009 A1AT_HUMAN Plasma P00736 C1R_HUMAN PlasmaP02647 APOA1_HUMAN Plasma P02751 FINC_HUMAN Plasma P09871 C1S_HUMANPlasma P00738 HPT_HUMAN Plasma P04114 APOB_HUMAN Plasma P00740 FA9_HUMANPlasma P0DOY2 IGLC2_HUMAN Plasma P02675 FIBB_HUMAN Plasma P00751CFAB_HUMAN Plasma P05543 THBG_HUMAN Plasma P02679 FIBG_HUMAN PlasmaP02790 HEMO_HUMAN Plasma P05155 IC1_HUMAN Plasma P02765 FETUA_HUMANPlasma P61769 B2MG_HUMAN Plasma P01834 IGKC_HUMAN Plasma P07225PROS_HUMAN Plasma P00338 LDHA_HUMAN Plasma P07195 LDHB_HUMAN PlasmaP00488 F13A_HUMAN Plasma P19652 A1AG2_HUMAN Plasma P00747 PLMN_HUMANPlasma P02747 C1QC_HUMAN Plasma P08519 APOA_HUMAN Plasma P43251BTD_HUMAN Plasma P02763 A1AG1_HUMAN Plasma P02741 CRP_HUMAN PlasmaP04275 VWF_HUMAN Plasma P02746 C1QB_HUMAN Plasma P17936 IBP3_HUMANPlasma P02745 C1QA_HUMAN Plasma P00742 FA10_HUMAN Plasma P04075ALDOA_HUMAN Plasma P01034 CYTC_HUMAN Plasma P05160 F13B_HUMAN PlasmaP02753 RET4_HUMAN Plasma P04070 PROC_HUMAN Plasma P06744 G6PI_HUMANPlasma P02766 TTHY_HUMAN Plasma P61626 LYSC_HUMAN Plasma P05062ALDOB_HUMAN Plasma P06276 CHLE_HUMAN Plasma P04278 SHBG_HUMAN PlasmaP02786 TFR1_HUMAN Plasma

Dynamic Range. The three-particle type panel was assessed for itsability to assay proteins in a sample across a wide dynamic range ofprotein concentrations. Feature intensities corresponding to proteinsthat were identified by mass spectrometry were compared to the valuesdetermined by other assays for the same protein at the sameconcentration. After mass spectrometry analysis and data processing, MS2peptide-spectral matches (PSM) were used to identify peptides andassociated proteins present in the corona of the distinct particlestypes in the particle panel. In parallel, peptides were also directlydetected in a plasma sample, without the use of the three-particle typepanel for corona analysis via the Proteograph workflow. Resultingpeptide features having the maximum MS-determined intensity of allobserved features, as determined using the OpenMS MS data processingtools to extract monoisotopic peak values, was selected for eachprotein. The MS-determined intensities were then modeled againstcomparable published abundance levels for the same proteins. FIG. 25shows a correlation between the maximum intensities of proteins indistinct coronas from the distinct nanoparticle types for each particletype in the three-particle type panel relative to plasma proteins andconcentration of the same proteins determined using other methods. Asshown by the regression model slopes and the intensity span of themeasured data, the particle coronas contained more protein hits at lowerabundances than does plasma. Additionally, the dynamic range of thosemeasured values was compressed, as shown by a reduced slope of theregression models, for particle measurements as compared to plasmameasurements, showing that particles effectively compressed the measureddynamic range of protein abundance in the corona as compared to inplasma. This may be attributed to a combination of absolute proteinconcentration, protein binding affinity to particles, and proteininteractions with neighboring proteins. These results indicate that themethods disclosed herein of using a multi-particle type panel forenrichment of proteins in distinct coronas corresponding to the distinctparticle types facilitated the identification of a broad spectrum ofplasma proteins, particularly those in the low abundance that arechallenging for rapid detection by conventional proteomic techniques.

Example 6 Precision of the Corona Analysis Assay

This example describes the precision of the coronal analysis assay.Precision, a measure of repeatability and reproducibility of an assay,was assessed by comparing multiple measurements under the sameconditions and determining the variability between the individualmeasurements. To investigate the reproducibility of the particle proteincorona analysis Proteograph workflow, the peptide MS feature intensitieswere extracted and compared from the three full-assay replicates foreach of the three particle types. The raw MS files for each replicatewere converted to mzML format, which is a standard, interchangeable MSfile format, using the msconvert.exe utility from the openMS suite ofprograms. Also using openMS processing pipeline, MS1 features wereextracted from the raw data and aligned into groups by overlappingretention time and mz value. Groups were selected which contained afeature from each of the three replicates, and filtered to remove thebottom decile based on the clustering algorithm's quality score (90% offeature groups retained for subsequent precision analysis). For theSP-003, SP-007, and SP-011 nanoparticles, a total of 2,744, 2,785, and3,209, respectively, clustered feature groups were used for theprecision analysis. The distribution of log-transformed raw intensitiesfor each of the replicates for these feature groups were plotted in theFIG. 26 with the value of each particle type displayed in the labeledpanel. As shown in FIG. 26, the feature data for each particle type washighly reproducible and this reproducibility was consistent across bothhigh intensity and low intensity features.

For a more quantitative assessment of performance, beyond visualinspection of raw data, the overall precision of the particle coronasafter quantile normalizing the group feature intensities was estimated.This normalization method was based on the assumption that all compareddistributions should be identical and accordingly adjusts theintensities for each compared distribution. This assumption isreasonable given that the reproducibility of the physicalcharacteristics of the particle types themselves and from separateanalyses of these particles (e.g., with X-ray photoelectronspectroscopy, high-resolution transmission electron microscopy, andother analytical methods). With the normalized values, the standarddeviations were evaluated and the coefficients of variation (CVs) weredetermined using the appropriate transformation of log-treated data. Foreach particle, the median CVs (percent of quantile normalized CV or QNCV%) are shown in TABLE 8. This result demonstrates that theparticle-measured protein MS feature intensities have sufficientprecision across the thousands of MS feature intensities observed todetect relatively small differences in reasonable small studies. Forexample, given a CV of 25%, there was approximately 100% power to detecttwo-fold changes with Bonferroni-corrected significance.

The overall precision of particle coronas was estimated by normalizingthe group feature intensities using quantile normalization, which makesthe prior assumption that all compared distributions should be identicaland adjusts the intensities for each compared distributionappropriately. With the normalized values, the standard deviations wereevaluated and the coefficients of variation (CVs) were determined usingthe appropriate transformation of log-treated data. For each particletype, the median CVs (percent of quantile normalized CV or QNCV %) areshown in TABLE 8. A low coefficient of variation (CV) was indicative ofa high degree of assay precision.

TABLE 8 Median QNCV % for precision evaluation of the particle proteincorona-based Proteograph workflow Particle Median QNCV % Count SP-003 232744 SP-007 29 2785 SP-011 20 3209

This result demonstrated that the particle-measured protein MS featureintensities had sufficient precision across the thousands of MS featureintensities observed to detect relatively small differences inreasonable small studies.

Example 7 Accuracy of the Corona Analysis Assay

This example describes the accuracy of the corona analysis assay. Theaccuracy of a method should be sufficiently robust for detecting a truedifference between groups of samples in biomarker discovery andvalidation studies. Accuracy of the corona analysis assay was determinedby comparing corona analysis assay results to those obtained by othermethods. To evaluate the corona analysis assay's accuracy, a spikerecovery study was performed using the SP-007 nanoparticles. C-reactiveprotein (CRP) was selected for analysis based on the measurement of itsendogenous levels. Using the enzyme-linked immunosorbent assay(ELISA)-determined endogenous plasma levels for CRP, known amounts ofthe purified protein (see Methods) were spiked to achieve testablemultiples of the endogenous levels. The CRP levels after spiking weredetermined empirically by ELISA to be 4.11, 7.10, 11.5, 22.0, and 215.0μg/mL for the 1× (unspiked), 2×, 5×, 10×, and 100× samples,respectively. The extracted MS1 feature intensities were plotted for thefour indicated CRP tryptic peptides detected by MS on the SP-007particles versus the CRP concentrations (FIG. 14A). FIG. 30 also showsthe accuracy of measurements for CRP proteins on the SP-007 particles ina spike-recovery experiment for four different peptides. The MS1 featureintensity cannot be detected for two of the peptides at the unspiked 1×concentration of CRP. The fitted lines were linear models using thegiven feature's spike intensities.

Fitting a regression model to all 4 of the CRP tryptic peptides resultedin a slope of 0.9 (95% CI 0.81-0.98) for the response of corona MSsignal intensity versus ELISA plasma level, which is close to a slope of1 that would be considered to be perfect analytical performance. Incontrast, a similar regression model fitted to 1,308 other (non-spiked)MS features identified in at least 4 of the 5 plasma samples, for whomthe signals from associated MS features should not vary across thesamples, had a slope of −0.086 (95% CI −0.1-−0.068). These resultsindicated the ability of that particle type to accurately describedifferences between samples will provide a useful tool to quantifypotential markers in comparative studies. If a protein level changes ina sample due to some factor, the methods disclosed herein will detect asimilar level change of protein bound to particle types of the particlepanel, which is a critical property of the present particle type to beeffective in any given assay. Moreover, the response of thespiked-protein peptide features also suggests that with appropriatecalibration, the particle protein corona method could be used todetermined absolute analyte levels as opposed to just relativequantitation.

Example 8 Proteomic Analysis of NSCLC Samples and Healthy Controls

This example describes proteomic analysis of NSCLC samples and healthycontrols. To demonstrate the potential utility of the corona analysisplatform, the platform's ability was evaluated using a single particletype, SP-007, and serum samples from 56 subjects (28 with Stage IV NSCLCand 28 age- and gender-matched controls) to observe differences betweenthe groups. The selected subject samples represented a reasonablybalanced study to identify potential MS features that are differentbetween the groups. The age and gender characteristics of the subjectsare summarized in TABLE 6 and full data on subject annotation includingdisease status and co-morbidities are compiled in TABLE 9.

TABLE 9 Gender and age information for the patients from whom the serumsamples were obtained Class Gender Mean Age Sd Age Number Control F 71.17.7 19 M 72.4 11.1 9 Diseased F 70.7 7.5 19 M 15.6 13.6 9

After collection and filtering of the MS1 features followed by log₂transformation of their intensity, the datasets were median scaledwithout respect to class.

FIG. 29 shows the normalized intensity distributions for all 56 subjectdatasets. All 56 sample MS raw data files from the NSCLC versus controlstudy were processed by OpenMS pipeline scripts to extract MS1 featuresand their intensities and cluster them into feature groups based onoverlapping mz and RT values within specified tolerances. Only thosefeature groups were retained that 1) had at least 50% presence of afeature in the group from at least one of the arms of the comparison and2) had a feature group cluster quality above the 25th percentile. Theretained features were median normalized without respect to class andused for subsequent univariate analytical comparison. There were nooutliers by inspection of the distributions and all datasets wereretained for the univariate analysis.

There did not appear to be any outlier datasets by inspection.Univariate comparison of feature group intensities between the classeswas performed with a non-parametric, Wilcoxon Test (two-sided). Theresulting p-value for the comparison was corrected for multiple testingusing the method of Benjamini-Hochberg. Using an adjusted p-valuecut-off of 0.05, a total of seven feature groups demonstratedstatistical significance, as summarized in FIG. 27.

All five of the proteins identified as differentially abundant betweenthe NSCLC-diseased and control groups have previously been implicated incancer if not actually NSCLC itself. PON1, or paraoxanase-1, has acomplicated pattern in lung cancer including the involvement of arelatively common minor allele variant (Q192R) as a risk factor. At theprotein level, PON1 is modestly decreased in lung adenocarcinoma. SAA1is an acute phase protein that has been shown to be overexpressed inNSCLC in MS-related studies, and the identified peptide was found to beincreased 5.4-fold in diseased subjects. The matrisome factor tenascin C(TENA) has been shown to be increased in primary lung tumors andassociated lymph node metastases compared with normal tissue, and theassociated MS feature was found to be increased by 2-fold in this study.Neural cell adhesion molecule 1 (NCAM1) serves as a marker fordiagnosing lung neuroendocrine tumors. FIBA peptides were identified byMS analysis with increased levels correlating with advancing progressionof lung cancer. Of special note are the two unknown features, Group2 andGroup7, which show differences between control and diseased subjects.Group2 was found in 54 out of 56 subjects and had a modest 33% decreasein diseased subjects. In contrast, Group7 was found only in diseasedsubjects (14 out of 28 members of the class). These results demonstratedthe potential utility for the particle corona to aid in identifyingknown and unknown markers for different disease states.

Example 9 Particle Panel for Assaying Proteins in a Sample

This example illustrates a 10-particle type particle panel for assayingproteins in a sample. This particle panel shown in TABLE 10 includes 10distinct particle types, which differ in size, charge, and polymercoating. All particle types in this particle panel aresuperparamagnetic. The panel shown in below was used to assay proteinsin samples.

TABLE 10 10 Particle Type Particle Panel Particle Mean DLS Mean Zeta IDParticle Description diameter (nm) Potential (mV) SP-003 Thick silicacoated 262 −36.9 SPION SP-006 N-(3- 232 20.9 Trimethoxysilylpropyl)diethylenetriamine SP-007 PDMAPMA-coated 259 25.8 SPION SP-010Carboxylated, PAA 366 −47.9 SP-353 Amino surface 606 27.2 microparticle,0.4-0.6 μm SP-333 Carboxylate 1300 −28.5 microparticle, surfactant freeSP-339 Polystyrene carboxyl 410 −31.4 functionalized SP-347 Silicacoated, 200 nm 281 −21.8 SP-365 Silica 231 −39.0 SP-373 Dextran basedcoating, 169 −0.5 0.13 μm

Protein coverage of V1 panel. To evaluate the total protein groupcoverage seen across multiple samples in a clinical sample set, plasmasamples from 16 individuals were evaluated for a panel of ten distinctparticle types shown in TABLE 10 and referred to as the V1 panel. usingthe sample preparation, MS data acquisition and MS data analysis methodsdescribed herein. A mix of non-small-cell lung carcinoma (NSCLC)patients and healthy individuals (n=8 for each) was used to provideadverse set of proteins and protein groups, present in both healthy andcancer cells, for analysis and identification using the methodsdescribed herein. At the 1% FDR (protein and peptide) rate, a total of2,009 protein groups were efficiently identified. For comparison, in thepreviously mentioned published study, 4,500 protein groups were detectedacross 16 individual plasma samples in a complex workflow comprised bymore than 70 steps and more than 30MS fractions per sample, likelytaking weeks to complete.

Example 10 10-Particle Type Particle Panel for Protein Assaying

This example illustrates the development of a 10-particle type particlepanel for methods of assaying proteins using biomolecule coronaanalysis, as described herein.

Particle Screen. To demonstrate the ability of the corona analysisplatform to expand its coverage through guided particle addition,biomolecule coronas from 43 particles types with distinctphysicochemical properties and screened in a similar manner to thethree-particle type particle panel disclosed herein.

TABLE 11 Particles for Screening DLS Zeta Diam- poten- Particle eter DLStial type Particle Description (nm) PDI (mV) SP-001 Carboxylated citratecoated  374 0.23 −34.0 SP-002 Phenol-formaldehyde resin coated  335 0.39−29.0 SP-003 Silica coated SPION  233 0.05 −36.9 SP-004 Polystyrenecoated  411 0.32 −45.7 SP-005 Carboxylate Poly(styrene-co-  247 0.19−36.5 methacrylic acid) SP-006 N-(3-Trimethoxysilylpropyl)diethylenetriamine coated  232 0.30  20.9 SP-007PDMAPMA-coated SPION  283 0.09  25.8 SP-0081,2,4,5-Benzenetetracarboxylic acid  426 0.43 −34.5 coated SP-009PVBTMAC coated  229 0.11  35.9 SP-010 Carboxylated, Polyacrylic acid 366 0.23 −47.9 SP-016 Titanium(IV) oxide coated 1623 0.92 −32.1 SP-019Phenylboronic acid coated  305 0.44 −36.4 SP-047 Poly(glycidylmethacrylate-benzylamine) 1255 0.54  18.1 coated SP-060 Maleimide basesurface  302 0.15 −40.8 SP-064 Poly(N43-  302 0.25  27.7(Dimethylamino)propyl]methacrylamide-co-[2-(methacryloyloxy)ethyl]dimethyl- (3-sulfopropyl)ammoniumhydroxide, P(DMAPMA-co-SBMA) coated SP-065 Modified Random 30 nt ssDNA 364 0.21 −43.5 SP-066 Smaller size carboxylated citrate coated  2100.25 −35.3 SP-369 Carboxylated, Original coating, 50 nm  104 0.15 −31.5SP-373 Dextran based coating, 0.13 μm  169 0.07  −0.6 SP-374 SilicaSilanol coated with lower acidity  225 0.11 −25.6 SP-389 BioMag ®PlusWheat Germ Agglutinin 3514 0.97 −21.6 coated microparticle SP-390 Oleicacid- Hydrophilic/hydrophobe  98 0.10 −38.0 surface SP-391 Rare earthdoped phosphor particles  130 0.15 −16.0 SP-392 Gadolinium oxidenanopowder coated 1199 0.82  −4.9 SP-393 Oligonucleotide-philic Apostle 614 0.23 −41.9 MiniMaxTM Magnetic Nanoparticles SP-394 Iron OxideNanoparticles with Azide  64 0.11 −17.0 Groups coating, 30 nm SP-397DEAE starch coated  99 0.18  13.6 SP-398 Poly(maleic acid-co-olefin)amphiphilic  393 0.31 −27.8 coating SP-399 Polyvinyl alcohol coated  1630.10  −8.9 SP-300 Poly(4-vinylpyridine) (P4VP) coated  177 0.21 −19.3SP-301 Poly-diallyldimethylamine coated, strong  114 0.14  24.6 anionexchanger SP-305 Amine small clusters  75 0.17  −9.5 SP-406 Boronatednanopowder surface  491 0.45 −40.7 SP-413 Nanotrap Blue VSA CS MagneticPorous 3500 0.77  −3.0 surface SP-333 Carboxylate microparticle,surfactant free 1348 0.66 −28.5 SP-339 Polystyrene carboxylfunctionalized  410 0.03 −31.4 SP-341 Carboxylic acid, 150 nm  154 0.10−26.0 SP-347 Silica coated, 200 nm  281 0.18 −21.8 SP-353 Amino surfacemicroparticle, 0.4-0.6 μm 1723 0.75  31.4 SP-356 Silica aminofunctionalized 2634 0.62  19.8 microparticle, 0.1-0.39 μm SP-363Jeffamine, 0.1-0.39 μm  253 0.13 −35.4 SP-364 Polystyrene microparticle,2.0-2.9 μm 3176 0.96 −55.9 SP-365 Silica  231 0.02 −39.0

The 43 particle types were evaluated using 6 conditions, as described inthe methods sections, and the most optimal conditions were used in asecondary analysis to select the best combination based on totalidentified protein number. The 43-particle type screen was conductedusing a plasma pool of healthy and lung cancer patients, different fromthe CRC pool used for the three-particle type particle panel, todemonstrate platform validation across biological samples. A pooledsample was used to increase protein diversity. Strict criteria were usedto identify potential proteins for panel selection and optimization. Formaximum potential evaluation, a protein had to be represented by atleast one peptide-spectral-match (PSM; 1% false discovery rate (FDR)) ineach of three full assay replicates to be counted as “identified.” Thepanel with the largest number of individual unique Uniprot identifierswas selected for the 10-particle type particle panel.

Protein Coverage of 10-Particle Type Particle Panel. Data disclosedherein confirms that the particle panels provided can be used todetermine changes in proteomic content across many biological samples.The particle panels disclosed herein have high precision and accuracyand provide methods that take an unbiased approach that doesn't requirespecific ligands to known proteins. Thus, these panels are particularlywell suited to biomarker discovery. The breadth and depth of plasmaprotein coverage using the 10-particle type panel was investigated.Using a database (n=5,304) of MS-derived plasma protein intensities (aclose correlate to concentration), the coverage of the 10-particle typepanel was compared against the full extent of the database as well asagainst the coverage obtained by MS evaluation of simple plasma (directMS analysis of the same plasma sample without particle-based sampling).FIG. 36 shows matching and coverage of a particle panel of the 10distinct particle types to a 5,304 plasma protein database of MSintensities. The ranked intensities for the database proteins are shownin the top panel (“Database”), the intensities for proteins from simpleplasma MS evaluation are shown in the second panel (“Plasma”) and theintensities for the optimal 10-particle panel are shown in the remainingpanels. The plasma protein intensities database is from Keshishian etal. (2015). Multiplexed, Quantitative Workflow for Sensitive BiomarkerDiscovery in Plasma Yields Novel Candidates for Early Myocardial Injury.Molecular & Cellular Proteomics, 14(9), 2375-2393. The results, shown inFIG. 36, confirmed and extended the results shown for the particle panelof 3 distinct particle types described above, which were used inprecision experiments shown FIG. 26. The particle panel of 10 distinctparticle types identified 1,598 proteins vs. 268 proteins for simpleplasma. Furthermore, each individual particle type detectedsubstantially more proteins than direct MS analysis of simple plasma.Unlike MS analysis on simple plasma, the particle panel of 10 distinctparticle types interrogated the entire spectrum of the concentration ofplasma proteins. Said differently, while the proteins identified fromthe simple plasma sample were skewed toward the higher intensityproteins (that is, higher abundance proteins), the proteins identifiedfrom the particle panel of 10 distinct particle types extended over 8orders of magnitude in dynamic range of the concentrations in thedatabase. Only 21 proteins in the database had intensities lower thanthe lowest protein matched from the particle panel of 10 distinctparticle types. As demonstrate in FIG. 36, the particle panel of 10distinct particle types demonstrated high precision, accuracy, and broadcoverage across a wide range of protein concentrations in plasma andenables broad-scale, unbiased proteomic analyses in parallel acrosslarge numbers of biological samples, and can match the cost and speed ofwhat is possible in genomic data acquisition today.

Precision of a Particle Panel Including 10 Distinct Particle Types. Thisexample describes reproducibility of particle corona for a particlepanel including 10 distinct nanoparticle types. Particles were analyzedto determine the coefficient of variation (CV) of each feature groupbetween the replicate runs for each particle type of the particle panelincluding 10 distinct nanoparticle types. A low CV indicated highprecision and reproducibility between replicate runs. The data wasprocessed using the software program OpenMS and retained feature groupswhich contained an observed precursor feature from each of threereplicates. The bottom 5% of the data was removed to eliminatestatistical outliers based on a quality score of the clusteringalgorithm. Group feature intensities were median normalized, and theoverall precision of the coronas of each particle type was estimated.Normalization was performed such that the overall median intensity foreach injection remained the same, and intensities were adjusted for eachcompared distribution to account for intensity shifts due to, forexample, overall differences in instrument response. Differences ininstrument response may arise in a variety of analysis methods,including X-ray photoelectron spectroscopy, high-resolution transmissionelectron microscopy, and other analytical methods. The normalized valuesof the coefficients of variation (CVs) of each feature group were thenevaluated for each particle type of the particle panel including 10distinct nanoparticle types. TABLE 12 shows the optimized panel of 10distinct particle types.

TABLE 12 10 Particle Type Particle Panel Particle Type ParticleDescription SP-333 Carboxylate microparticle, surfactant free SP-339Polystyrene carboxyl functionalized SP-347 Silica coated, 200 nm SP-365Silica SP-373 Dextran based coating, 0.13 μm SP-390 Oleic acid-Hydrophilic/hydrophobe surface SP-406 Boronated nanopowder surfaceSP-007 PDMAPMA-coated SPION SP-047 Poly(glycidylmethacrylate-benzylamine) coated SP-064 Poly(N-[3-(Dimethylamino)propyl]methacrylamide-co-[2-(methacryloyloxy)ethyl]dimethyl- (3-sulfopropyl)ammoniumhydroxide, P(DMAPMA-co-SBMA) coated

TABLE 13 shows the median percent of quantile normalized CV (QNCV %) forprecision evaluation of the protein corona-based Proteograph workflowfor plasma and a particle panel including 10 distinct particle types forfeatures, peptides and proteins. A 1% peptide and 1% protein falsediscovery rate (FDR) was applied. Data was processed using MaxLFQanalysis software, applying the condition that each protein group haveat least one peptide ratio-count and detection in all replicates, whichreduced the number of groups used for the precision analysis. For eachparticle type of the particle panel including 10 distinct nanoparticletypes, the median CVs, including percent of quantile normalized CV orQNCV %, are shown in TABLE 13. A similar analysis was performed at apeptide and protein level using MaxQuant to align identifiable featuregroups to features, peptides, and proteins (TABLE 13). The number ofidentifiable features decreases from features to peptides to proteins,as peptides can comprise multiple features and proteins can comprisemultiple peptides. This nanoparticle panel detected 1,184 protein groupswith a 1% false discovery rate (FDR).

TABLE 13 Median QNCV % for a particle panel including 10 distinctnanoparticle types Features Peptides Proteins (OpenMS) (MaxQuant)(MaxQuant) # Median # Median # Median Particle Features CV Peptides CVProteins CV Plasma 2141 22.5 976 22.7 162 17.1 SP-333 2163 17.2 119220.5 250 18.2 SP-339 2330 19.4 1406 20.8 296 17.9 SP-347 2792 15.4 210519.9 469 16.4 SP-365 2322 17.9 1867 22.4 447 18.4 SP-373 2796 27.1 209130.3 479 25.5 SP-390 2267 29.3 1265 25.8 216 19.1 SP-406 3823 28.7 194730.8 410 28.3 SP-007 2351 21.1 1292 21.5 250 17.1 SP-047 2233 36.5 117635.7 279 30.8 SP-064 2984 20.2 2112 23.3 433 19.3

Coefficients of variation (CVs) were examined at the level of features,peptides and proteins independently. Analysis of feature, peptide, andprotein CVs provide complementary views of assay precision. OpenMS andMaxQuant software engines were used for feature, peptide, and proteinmatching. MaxQuant was used to for protein grouping with FDR. OpenMS wasused to perform peptide-spectrum-matching (PSM) using the X!Tandemmatching tool. MaxQuant was configured to use the Andromeda algorithm.Peptide CVs and protein CVs were used to assess precision of theplatform for use with biological variables. The mean CV decreased withincreasing peptide size, such that the mean CV was lower for peptidesthan for proteins. The particles maintain a CV similar to plasma, whileparticles have higher occurrences of features, peptides, and proteinsthan plasma. In particular, the number of proteins on particles of anygiven particle type is higher than plasma (average: 218% higher, range:133%-296% higher) while maintaining a comparable CV (21.1% vs 17.1% forparticles and plasma, respectively). Furthermore, the panel of theparticle types identified 1,184 proteins while only identifying 162proteins for plasma alone.

Accuracy of a Particle Panel Including 10 Distinct Nanoparticle Types.The accuracy of for the particle panel including 10 distinctnanoparticle types to detect a real difference between groups of samplesin biomarker discovery and validation studies was assessed. Accuracy wasdetermined by measuring spike recovery data in the presence ananoparticle types SP-007, and C-reactive protein (CRP). Spike recoverydata was further measured in the presence of one three additionalpolypeptides (S100A8/9, and Angiogenin) in combination with each ofthree particle types (SP-006, SP-339, SP-374). Known amounts of eachpolypeptide were spiked in at different concentrations, increasing byfactors of 10 (e.g., 1×, 2×, 5×, 10×, and 100×). The level of eachpolypeptide was measured by ELISA. Derived peptide and proteinintensities were plotted against the ELISA protein concentration.Peptide intensities were derived using OpenMS MS1/MS2 pipeline to findclustered feature groups that have a target protein MS2 ID assigned toat least one feature within the cluster. Only cluster groups withrepresentation in at least one replicate for the top spike levels wereused for the analysis. Protein intensities were derived using theMaxQuant software. Intensity values for each protein were summarized.and the data was scaled such that the maximal concentration was 2. MSdatasets were performed in triplicate for each spike concentration(e.g., 1×, 2×, 5×, 10×, and 100×), providing 15 individual protein orpeptide measurements. Not all peptides were detected in all particletypes or particle type replicates. Results of the MS datasets are shownin FIG. 31-34. FIG. 31 shows the accuracy of peptide featuremeasurements of Angiogenin in a spike-recovery experiment. FIG. 32 showsthe accuracy of peptide feature measurements of S10A8 in aspike-recovery experiment. FIG. 33 shows the accuracy of peptide featuremeasurements of S10A9 in a spike-recovery experiment. FIG. 34 shows theaccuracy of peptide feature measurements of CRP in a spike-recoveryexperiment. The fitted lines are linear fits to the spike intensities ofeach feature.

FIG. 31-34 illustrate the results of three spike recovery experiments todetermine the accuracy of peptide feature measurements of Angiogenin,S10A8, S10A9, and CRP, respectively. The data demonstrated high degreesof correlation between individual measurements for peptides (mean r² is0.81) and proteins (mean r² is 0.97). The mean slope across all proteinsis 1.06. TABLE 14 showed the r² correlation per comparison and also themean r² correlation per protein. Out of 20 peptides, only two showed nocorrelation between ELISA assays on two different particles types, inwhich one peptide presented in two charge states. The aberrationsdecreased with increasing peptide size, such that the frequency ofaberrations was lower for peptides than for proteins. The two peptidesthat showed now correlation with the ELISA on two different particlesshowed a high degree of correlation to ELISA in the other particletypes. The offending peptide may be co-eluting with another peptide thatmasks its signal, for example through charge stealing.

TABLE 14 provides a summary of regression fits to protein intensity asmeasured by corona analysis or ELISA. Values are shown for individualparticle types and averaged between four repeats per particle type. Theprotein concentrations, as measured by corona analysis, were consistentacross a range of conditions and a range of particle types. As shown inTABLE 14, protein measurements were well correlated, as shown by high r²values (mean 0.97, range across individual particles 0.92-1.0; rangeaveraged across particles 0.94-0.99). This consistent behavior acrossthe four proteins as measured by an ELISA illustrates the accuracy ofthe corona analysis assay.

TABLE 14 Summary of protein intensity regression fit. Particle ProteinType intercept slope r_sq adj_r_sq intercept slope r_sq adj_r_sq ANGSP-006 0.16 1.05 0.94 0.91 0.30 0.96 0.97 0.95 ANG SP-007 0.75 0.78 0.930.90 ANG SP-339 0.05 1.05 1.00 1.00 ANG SP-374 0.23 0.98 0.99 0.99 CRPSP-006 −0.24 0.96 1.00 1.00 −0.85 1.22 0.99 0.99 CRP SP-007 −0.48 1.070.99 0.99 CRP SP-339 −1.08 1.31 0.99 0.98 CRP SP-374 −1.60 1.54 NA NAS100A8 SP-006 −0.12 1.02 1.00 1.00 0.03 0.98 0.97 0.95 S100A8 SP-007−0.20 1.12 0.92 0.89 S100A8 SP-339 0.34 0.81 0.99 0.98 S100A8 SP-3740.10 0.96 0.96 0.95 S100A9 SP-006 −0.56 1.34 0.90 0.87 −0.09 1.06 0.940.92 S100A9 SP-007 −0.44 1.27 0.93 0.91 S100A9 SP-339 0.51 0.68 0.980.97 S100A9 SP-374 0.11 0.96 0.95 0.93

Comparison to other platforms. The methods disclosed herein usingmulti-particle types panels to enrich proteins in distinct coronascorresponding to each protein type in the panel (e.g., corona analysisusing the Proteograph workflow) provides wide and unbiased coverage ofprotein identification in the proteome. Other methods that attempt broadcoverage of the proteome require multiple fractionation steps, complexworkflows, and are slow in comparison to the methods presented herein.Other methods lack the breadth and impartiality of the methods disclosedherein and are compared herein to the presently disclosed methods ofassaying proteins.

Geyer et al (Cell Systems 2016) utilized a rapid shotgun proteomicsapproach and yielded an average of 284 protein groups per assay and 321protein groups across all replicates. The assessment utilized a slower,multi-day protocol with fractionation that yielded approximately 1,000protein groups. No replicates were performed, likely due to prohibitivecosts and time requirements, and so no variance could be determined.

Geyer used a short run to generate 321 protein groups, and the CV ofeach protein was determined. The 321 groups assessed by Geyer and the1,184 protein groups identified by the 10 particle type panel comprised88 protein groups in common between the two methods. As protein groupsmay comprise multiple related proteins which may be differentiallycombined based on the detected peptides, identification of 88 commonprotein groups is unexpectedly high.

For the 88 common protein groups, the data from Geyer et al. wasanalyzed, and a median CV of 12.1% was determined. In contrast, the same88 common protein groups, as analyzed by Proteograph, had a lower CV ofonly 7.2%. Thus, the instant methods of corona analysis usingmulti-particle type panels and the Proteograph workflow providedimproved precision over the methods of Geyer et al. Additionally, Geyeret al.'s assessment showed an r² indicative of assay accuracy, of 0.99for 4 proteins. Similarly, the Proteograph assay showed an r² of 0.97.

Geyer et al. further assessed the number of protein groups with CVs<20%, the commonly used cutoff for in vitro diagnostic assays. Theparticle panel methods detected 761 protein groups with CV<20% which was3.7 times greater than the number identified by Geyer et al. A furtherassessment by Dr. Mann (Niu et al, 2019) identified 272 protein groupswith CV <20%, 2.8-fold lower than the number identified by the multiparticle type panels and methods of use thereof disclosed herein.

Bruderer et al. assessed protein group CV's using data generated by aBiognosys platform (Bruderer et al, 2019). This assessment identified465 proteins, wherein those 465 proteins had a median CV of 5.2% and 404of those proteins had CVs <20%. In contrast, the best 465 proteins fromthe 1,184 proteins identified using the methods disclosed herein had amedian CV of 4.7% and 761 of the 1,184 proteins identified byProteograph had CV's <20%.

In comparison to the assessments of Geyer et al., Niu et al, andBruderer et al., the instant particle panels provided improved CVs foran equivalent number of proteins as well as number of proteins meeting aCV threshold, over other identification methods. The methods disclosedherein additionally have reduced bias relative to other methods, such astargeted mass spectrometry and other analyte specific reagents (e.g.,Olink). Such approaches measure a small number of pre-selected proteins,thereby introducing bias during the protein panel selection process. Asa result, these approaches have low CVs and high r² for the proteins ontheir panel as compared to the proteins identified by Proteograph andare limited to detecting proteins on the panel.

Example 11 Materials and Methods for Particle Synthesis

This example describes materials and methods for particle synthesis.

Materials. Iron (III) chloride hexahydrate ACS, sodium acetate(anhydrous ACS), ethylene glycol, ammonium hydroxide 28˜30%, ammoniumpersulfate (APS) (≥98%, Pro-Pure, Proteomics Grade), ethanol (reagentalcohol ACS) and methanol (≥99.8% ACS) were purchased from VWR.N,N′-Methylenebisacrylamide (99%) was purchased from EMD Millipore.Trisodium citrate dihydrate (ACS reagent, ≥99.0%), tetraethylorthosilicate (TEOS) (reagent grade, 98%), 3-(trimethoxysilyl)propylmethacrylate (MPS) (98%) and poly(ethylene glycol) methyl ethermethacrylate (OEGMA, average Mn 500, contains 100 ppm MEHQ as inhibitor,200 ppm BHT as inhibitor) were purchased from Sigma-Aldrich.4,4′-Azobis(4-cyanovaleric acid) (ACVA, 98%, cont. ca 18% water) anddivinylbenzene (DVB, 80%, mixture of isomers) were purchased from AlfaAesar and purified by passing a short silica column to remove theinhibitor. N-(3-Dimethylaminopropyl)methacrylamide (DMAPMA) waspurchased from TCI and purified by passing a short silica column toremove the inhibitor. The ELISA kit to measure human C-reactive protein(CRP) was purchased from R&D Systems (Minneapolis, Minn.). Human CRPprotein purified from human serum was from Sigma Aldrich.

Synthesis of superparamagnetic iron oxide nanoparticle (SPION)-basedSP-003, SP-007, and SP-011. The iron oxide core was synthesized viasolvothermal reaction (FIG. 28A-E, at top (FIG. 28A)) (Liu, J., et al.Highly water-dispersible biocompatible magnetite particles with lowcytotoxicity stabilized by citrate groups. Angew Chem Int Ed Engl 48,5875-5879 (2009); Xu, S., et al. Toward designer magnetite/polystyrenecolloidal composite microspheres with controllable nanostructures anddesirable surface functionalities. Langmuir 28, 3271-3278 (2012)).Typically, about 26.4 g of iron (III) chloride hexahydrate was dissolvedin about 220 mL of ethylene glycol at about 160° C. for ˜10 min undermixing. Then about 8.5 g of trisodium citrate dihydrate and about 29.6 gsodium acetate anhydrous were added and fully dissolved by mixing forabout an additional 15 min at about 160° C. The solution was then sealedin a Teflon-lined stainless-steel autoclave (300 mL capacity) and heatedto about 200° C. for about 12h. After cooling down to room temperature,the black paramagnetic product was isolated by a magnet and washed withDI water 3-5 times. The final product was freeze-dried to a black powderfor further use.

The silica-coated iron oxide nanoparticles (SP-003) were preparedthrough a modified Stober process as reported before (FIG. 28B)(Deng,Y., Qi, D., Deng, C., Zhang, X. & Zhao, D. Superparamagnetichigh-magnetization microspheres with an Fe₃O₄@SiO₂ core andperpendicularly aligned mesoporous SiO₂ shell for removal ofmicrocystins. J Am Chem Soc 130, 28-29 (2008); Teng, Z. G., et al.Superparamagnetic high-magnetization composite spheres with highlyaminated ordered mesoporous silica shell for biomedical applications. JMater Chem B 1, 4684-4691 (2013)). Typically, about 1 g of the SPIONswere homogeneously dispersed in the mixture of ethanol (about 400 mL),DI water (about 10 mL), and concentrated ammonia aqueous solution (about10 mL, 28-30 wt %), followed by the addition of TEOS (about 2 mL). Afterstirring at about 70° C. for about 6 h, amorphous silica coated SPIONs(denoted as Fe₃O₄@SiO₂) were obtained and washed 3 times with methanoland additional 3 times with water and the final product was freeze-driedto a powder.

To prepare SP-007 (PDMAPMA-modified SPION) and SP-011 (PEG-modifiedSPION), vinyl group functionalized SPIONs (denoted as Fe₃O₄@MPS) werefirst prepared through a modified Stober process as previously reported(FIG. 28C) (Crutchfield, C. A., Thomas, S. N., Sokoll, L. J. & Chan, D.W. Advances in mass spectrometry-based clinical biomarker discovery.Clin Proteomics 13, 1 (2016)). Briefly, about 1 g of the SPIONs washomogeneously dispersed under the aid of vortexing (or sonication) inthe mixture of ethanol (about 400 mL), DI water (about 10 mL), andconcentrated ammonia aqueous solution (about 10 mL, 28-30 wt %),followed by the addition of TEOS (about 2 mL). After stirring at about70° C. for about 6 h, about 2 mL of 3-(trimethoxysilyl)propylmethacrylate was added into the reaction mixture and stirred at about70° C. overnight. Vinyl functionalized SPIONs were obtained and washed 3times with methanol and additional 3 times with water and the finalproduct was freeze-dried to a powder. Next, for synthesis ofpoly(dimethyl aminopropyl methacrylamide) (PDMAPMA)-coated SPIONs(denoted as Fe₃O₄@PDMAPMA, SP-007 in FIG. 28D), about 100 mg ofFe₃O₄@MPS were homogeneously dispersed in about 125 mL of DI water.After bubbling with N₂ for about 30 min, about 2 g ofN-[3-(dimethylamino)propyl] methacrylamide (DMAPMA) and about 0.2 g ofdivinylbenzene (DVB) were added into the Fe₃O₄@MPS suspension under N₂protection. After the resulting mixture was heated to about 75° C.,about 40 mg of ammonium persulfate (APS) in about 5 mL DI water wasadded and stirred at about 75° C. overnight. After cooling down,Fe₃O₄@PDMAPMA were isolated with a magnet and washed 3-5 times withwater. The final product was freeze-dried to a dark brown powder. Forsynthesis of poly(ethylene glycol) (PEG)-coated SPIONs (denoted asFe₃O₄@PEGOMA, SP-011 in FIG. 28E), about 100 mg of Fe₃O₄@MPS werehomogeneously dispersed in about 125 mL of DI water. After bubbling withN₂ for about 30 min, about 2 g of poly(ethylene glycol) methyl ethermethacrylate (OEGMA, average Mn 500) and about 50 mg ofN,N′-Methylenebisacrylamide (MBA) were added into the Fe₃O₄@MPSsuspension under N₂ protection. After the resulting mixture was heatedto about 75° C., about 50 mg of 4,4′-azobis(4-cyanovaleric acid) (ACVA)in about 5 mL ethanol was added and stirred at about 75° C. overnight.After cooling down, Fe₃O₄@POEGMA were isolated with a magnet and washed3-5 times with water. The final product was freeze-dried to a dark brownpowder.

Example 12 Patient Samples

This example describes patient samples used in the present disclosure. Aset of 8 colorectal cancer (CRC) plasma samples with 8 age- andgender-matched controls was purchased from BioIVT (Westbury, N.Y.). Aset of 28 non-small cell lung cancer (NSCLC) serum samples with 28controls matched by age and gender was also obtained from BioIVT. Thedetailed information regarding the CRC/NSCLC patient samples andcontrols are shown in TABLE 15 and TABLE 16.

TABLE 15 NSCLC and Controls Class Age Gender Diagnosis MedicationsDiseased 53 Female Non Small Cell Lung Alimta 800 mg/Carboplatin 760 mg,Advil Cancer (NSCLC) 200 mg, Compazine 10mg, Dexamethasone 4 mg,Diclofenac Sodium 50 mg, Dicyclomine 10 mg, Folic Acid 1 mg, Lactulose10 g/15 ml, Lansoprazole 30 mg, Multivitamin, Oxycodone 5 mg, Reglan 10mg, Vitamin C 1000 mg, Vitamin D2 50000 iu Diseased 64 Female Non SmallCell Lung Opdivo, Alendronate Sodium 10 mg, Cancer (NSCLC), AllegraAllergy 180 mg, Anoro Ellipta Vitamin B Deficiency, 62.5 mcg-25 mcg,Aspirin 81 mg, Bystolic Hypertension (HTN), 5 mg, Calcium and D 500mg-200 iu, Hyperlipidemia Compazine 10 mg, Crestor 40 mg, Dilaudid 1200mg, Emla 2.5%-2.5%, Erythromycin 5 mg, Fish Oil 340 mg-1000 mg, FlonaseAllergy Relief 50 mcg, Hydromorphone 4 mg, Isosorbide Mononitrate 60 mg,Levothyroxine 75 mcg, Lisinopril 20 mg, Medical Marijuana, Multivitamin9 mg-Iron 15 ml, Neurontin 300 mg, Nitro, Oxycodone 5 mg, Plavix 75 mg,Protonix 20 mg, Unisom 25 mg, Ventolin 90 mcg, Vitamin D3 5000 iu, Xanax1 mg Diseased 73 Female Non Small Cell Lung Carboplatin/Paclitaxel,Acetaminophen Cancer (NSCLC), 325 mg, Multivitamin 1000 mg, CymbaltaImpaired Fasting 60 mg, Eliquis 5 mg, Guaifenesin Glucose (IFG), 100mg/5 ml, Neurontin 300 mg, Synthroid Pulmonary Nodule 100 mcg, Zofran 8mg Diseased 75 Female Non Small Cell Lung Osimertinib, Colace 100 mg,Flonase Cancer, Pneumothorax 50 mcg, Zofran 8 mg, Restasis 0.05%, Norco5 mg-325 mg, Megace 400 mg/10 ml, Tagrisso 80 mg Diseased 65 Female NonSmall Cell Lung Ceritinib 150 mg, Cipro 500 mg, Excedrin Cancer (NSCLC),500 mg, Lasix 40 mg, Glimepiride 4 mg, Type 2 Diabetes, Lamotrigine 200mg, Metformin 1000 mg, Multiple Sclerosis Naproxen 500 mg, Zofran 8 mg,Slow Release Iron 142 mg Diseased 94 Male Non Small Cell Lung Keytruda100 mg/4 ml, Betamethasone Cancer (NSCLC), Dipropionate 0.05%, Eliquis2.5 mg, Anemia (CKD), Fludrocortisone 0.1 mg, Folic Acid 1 mg, ChronicKidney Lomotil 2.5 mg-0.025 mg, Midodrine 10 mg, Disease (CKD), Omega Q,Prednisone 5 mg, Ranitidine Hyperlipidemia 150 mg, Simvastatin 40 mg(HLD), Prostate Cancer Diseased 65 Female Non Small Cell Lung Atenolol50 mg, Biotin 2500 mcg, Melatonin Cancer (NSCLC), 3 mg, Mometasone 0.1%,Vitamin D3 Hypertension (HTN) 1000 iu, Zofran 8 mg Diseased 79 FemaleNon Small Cell Lung Amlodipine 5 mg, Amoxicillin 875 mg, Cancer (NSCLC),Estradiol 0.01%, Folic Acid 1 mg, Januvia Type 2 Diabetes, 100 mg,Lidocaine/Prilocaine 2.5%, Hypercholesterolemia, Losartan HCL 50 mg,Nitrofurantoin Emphysema 100 mg, Pantoprazole 20 mg, Simvastatin 40 mg,Urinary Pain Relief 95 mg, Zofran 8 mg, Gemcitabine, CarboplatinDiseased 57 Male Non Small Cell Lung Carboplatin/Etoposide, Norvasc 10mg, Cancer (NSCLC), Lotensin HCT 20 mg-12.5 mg, Celexa Lung Mass,Primary 20 mg, Mycelex 10 mg, Lasix 20 mg, Norco Adenocarcinoma of 7.5mg-325 mg, Magnesium 400 mg, Lower Lobe of Right Melatonin Gummies 2.5mg, Metformin Lung, Brain 750 mg, Mycostatin 100,000 iu/mL, ZofranMetastases, 8 mg, Potassium Chloride 20 mEq, Hypokalemia Compazine 10 mgDiseased 63 Female Non Small Cell Lung Alimta, Carboplatin,Calcium-Vitamin D, Cancer (NSCLC), Folvite 1 mg, Keppra 500 mg,Synthroid Hypertension (HTN), 125 mcg, Prilosec 20 mg, Zofran 8 mg,Hypercholesterolemia, Compazine 10 mg, Zocor 40 mg GastroesophagealReflux Disease (GERD), Diverticulitis, Disease of Thyroid, Arhropathy,Actinic Keratosis Diseased 77 Male Non Small Cell Lung Singulair 10 mg,Meclizine HCL 25 mg, Cancer (NSCLC), Xarelto 20 mg, Synthroid 125 mcg,Miralax Hypertension (HTN), 17 g, Lidocaine 5%, Arnuity ElliptaMyelodysplastic 100 mcg, Medipro Vegan Chocolate Syndromes, Anemia 23.28oz, Exos Catalyte, Zinc Picolinate (Iron), 15 mg, Albuterol Sulfate 90mcg, Vitamin Hypothyroidism, B12/Folic Acid 500 mcg/400 mcg ProstateCancer, Bradycardia Diseased 70 Female Non Small Cell LungAlimta/Carboplatin/Keytruda/Neulasta, Cancer (NSCLC), Decadron 4 mg,Breo Ellipta Hypertension (HTN), 200 mcg/25 mcg, Folvite 1 mg, Lasix 40mg, Polycythemia, Left Neurontin 300 mg, Emla, Zestril 5 mg, LowerExtremity Magic Mouthwash, Glucophage 500 mg, Edema, Cellulitis of Aleve220 mg, Zofran 8 mg, Potassium Left Lower Extremity Chloride 10 meq,ProAir HFA 108 mcg, Spiriva 18 mcg, Valtrex 1000 mg Diseased 70 FemaleNon Small Cell Lung Taxotere 75 mg-Cyramza 10 mg-Nuelasta, Cancer(NSCLC), Ativan 0.5 mg, Basaglar 100 iu/mL, Hypertension (HTN),Dexamethasone 4 mg, Eliquis 5 mg, Type 2 Diabetes Fentanyl 25 mcg, FolicAcid 1 mg, Glimepiride 2 mg, Hydrochlorothiazide 12.5 mg, Lorazepam 0.5mg, Magnesium 300 mg, Metformin 1000 mg, Multivitamin 9 mg Iron/15 mL,Oxycodone 5 mg, Tramadol 50 mg, Trazodone 50 mg, Vitamin D3 2000 iuDiseased 78 Female Non Small Cell Lung Folic Acid 1 mg, Lasix 20 mg,Atarax 25 mg, Cancer (NSCLC), Hydroxyzine 25 mg, Klor Con 10 meq,Hypothyroidism Emla, Methylprednisolone 4 mg, Zofran 8 mg, Synthroid 100mcg, Kenalog 0.025% Diseased 68 Female Non Small Cell Lung Abraxane 100mg, Procrit 40000 iu, Aspirin Cancer (NSCLC), 325 mg, Benadryl 25 mg,Calcium 500 mg, Leukocystosis, Clopidogrel 75 mg, Codeine- GuaifenesinHypercalcemia, 10 mg-100 mg/5 ml, Imodium 2 mg, Iron Asthma, Major 325mg, Klor-Con 20 meq, Lasix 20 mg, Depressive Disorder, Levothyroxine 50mcg, Metformin 500 mg, Hypothyroidism, Niacin 500 mg, Ondansetron 8 mg,Proventil Hyperlipidemia, Type 90 mcg, Spiriva 18 mcg, Symbicort 160mcg- 2 Diabetes 4.5 mcg, Tylenol 500 mg, Xanax 0.5 mg, Zolpidem 10 mgDiseased 78 Male Non Small Cell Lung Xgeva 120 mg, Keytruda,Atorvastatin Cancer (NSCLC), 40 mg, Digoxin 125 mcg, Furosemide 40 mg,Lymphadenitis, Lexapro 20 mg, Medrol 4 mg, Metoprolol Hypertension(HTN), Tartrate 100 mg, Namzaric 21 mg-10 mg, Hyperlipidemia, AtrialNoxylane 500 mg, Vitamin D2 50000 iu, Fibrillation (AF), Warfarin 2 mgMalignant Neoplasm of Left Main Bronchus Diseased 79 Female Non SmallCell Lung Feraheme Non-ESRD, Amlodipine 5 mg, Cancer (NSCLC), Aspirin 81mg, Atenolol 50 mg, Bayer Pancytopenia, Liver Aspirin 325 mg,Calcium/Vitamin D3 Cirrhosis, Zoster, 1250 mg, Caltrate/Vitamin D3 1500mg, Neuralgia, Neuritis, Cartia XT 180 mg, Crestor 20 mg, DuragesicEssential Primary 25 mcg, Eliquis 5 mg, Folic Acid 1 mg, HypertensionGabapentin 300 mg, Oxycodone/Acetaminophen 5 mg/325 mg, Percocet 5mg/325 mg, Prednisone 1 mg, Procrit 40000 iu/ml, Tessalon Perles 100 mg,Vitamin D2 50000 iu Diseased 79 Male Non Small Cell Lung Octagam Liquid10%, Calcium 600 mg, Cancer (NSCLC), Digoxin 125 mcg, Folic Acid 400mcg, Prostate Cancer, Metoprolol Tartrate 25 mg, Probiotic, ImmuneRosuvastatin 10 mg Thrombocytopenic Purpura, HTN, HyperlipidemiaDiseased 54 Male Non Small Cell Lung Pembrolizumab, Dexamethasone 4 mg,Cancer (NSCLC), Glipizide 5 mg, Hydrocodone- Type 2 Diabetes,Acetaminophen 10 mg-325 mg, Ipratropium Hypertension (HTN) Bromide 17mcg, Lantus, Lisinopril 10 mg, Metformin 500 mg, Pravastatin Sodium 40mg Diseased 69 Male Non Small Cell Lung Aloxi 0.25 mg/5 ml, Cardizem 120mg, Cancer (NSCLC), Crestor 20 mg, Digoxin 250 mcg, Eliquis UnilateralPrimary 5 mg, Furosemide 20 mg, Glucosamine Osteoarthritis of LeftChondroitin PLUS Knee, Essential 375 mg/100 mg/36 mg/54 mg, Metformin ERPrimary Hypertension, 750 mg, Potassium Chloride ER 10 meq, Type 2Diabetes Zofran 8 mg Diseased 81 Male Non Small Cell Lung IpratropiumAlbuterol 0.5 mg/3 mg, Cancer (NSCLC), Metoprolol 50 mg, Coumadin 7.5mg, Vascular Dementia, Atorvastatin 80 mg, Lovenox 80 mg/0.8 ml,Hypertension (HTN), Magace ES 625 mg/5 ml, Lexapro 10 mg Lipid DiseaseDiseased 70 Female Non Small Cell Lung Nplate, Procrit 40,000 iu, Alimta500 mg, Cancer (NSCLC), Ferrous Sulfate 325 mg, Folic Acid 1 mg, AnemiaMedrol 4 mg, Metformin 500 mg, (Antineoplastic Simvastatin 40 mg,Tudorza Pressair Chemotherapy), 400 mcg Idiopathic Thrombocytopenia(ITP), Malignant Neoplasm of Upper Left Lobe, Vitamin B12 Deficiency,Folic Acid Deficiency, Asthma, Type 2 Diabetes, Hyperlipidemia (HLD),Hypertension (HTN), Hypercholesterolemia Diseased 76 Female Non SmallCell Lung Biotin 300 mcg, Cleocin 1%, Fenofibrate Cancer (NSCLC), 160mg, Flonase 50 mcg, Medrol 4 mg, Anemia (Iron Ranitidine 150 mg,Tagrisso 80 mg, Tarceva Deficiency), , Impaired 150 mg, Ventolin HFA 90mcg, Vitamin D2 Fasting Glucose (IFG) 50000 iu Diseased 91 Male NonSmall Cell Lung Clotrimazole 1%, Dextran, Digoxin Cancer (NSCLC), 125mcg, Furosemide 20 mg, Hydrocodone- Anemia (Iron Homatropine 5 mg-1.5mg/5 ml, Lasix 20 mg, Deficiency), Levothyroxine 25 mcg, MetoprololHypothyroidism, HTN Succinate 25 mg, Miracle Mouthwash, Mucinex 30mg-600 mg, Nystatin 100000 iu, Omeprazole 20 mg, Oxycodone 20 mg,Pravastatin 10 mg, Prednisone 10 mg, Proair 90 mcg, Procto-Med 2.5%,Relistor 150 mg, Sodium Chloride 1 g Diseased 69 Female Non Small CellLung Ativan 0.5 mg, Trazodone 50 mg Cancer (NSCLC), Anemia (IronDeficiency), Vitamin B12 Deficiency, T- Cell Prolymphocytic Leukemia,Hypertension (HTN) Diseased 65 Female Non Small Cell Lung Dexamethasone4 mg, Emla 2.5%, Cancer (NSCLC), Loperamide 2 mg, Lorazepam 1 mg,Chronic Obstructive Nystatin 100000 iu/ml, Ondansetron 4 mg, PulmonaryDisease Oravig 50 mg, Symbicort 160 mcg/4.5 mcg, (Emphysema), VentolinHFA 90 mcg, Navelbine 30 mg Cardiovascular Disease, OsteoporosisDiseased 77 Female Non Small Cell Lung Avastin 15 mg/kg, Alimta Cancer(NSCLC) 500 mg/Carboplatin/Neulasta then Alimta 500 mg, Aspirin 81 mg,Chantix 1 mg, Dexamethasone 4 mg, Folic Acid 1 mg, Instaflex, Metformin500 mg, Quinapril 40 mg, Simvastatin 20 mg, Vitamin D3 2000 iu, Zofran 8mg Diseased 85 Female Non Small Cell Lung Aletinib Cancer (NSCLC)Control 53 Female Normal Donor None Control 64 Female Normal Donor NoneControl 67 Female Normal Donor, Atorvastatin 40 mg HyopercholesterolemiaControl 72 Female Normal Donor None Control 73 Female Normal Donor,Prolia, Aciphex 20 mg Osteoarthritis (OA) Control 87 Male Normal Donor,Donor Vitamin B Complex, Zinc 50 ml, Alka with Fever Seltzer, VitaminB6, Vitamin B1, Vitamin B12, Pepsin 40 mg Control 65 Female NormalDonor, None Hypercholesterolemia Control 80 Female Normal Donor NoneControl 57 Male Normal Donor Multivitamin Control 63 Female Normal DonorNone Control 75 Male Normal Donor, Lisinopril 2.5 mg Hypertension (HTN)Control 70 Female Normal Donor None Control 70 Female Normal DonorMultivitamin 1000 mg Control 77 Female Normal Donor Lipitor 20 mg,Prevacid 20 mg Control 68 Female Normal Donor, Lisinopril 10 mgHypertension (HTN) Control 73 Male Normal Donor Tamsulosin HCL,Finasteride 5 mg Control 81 Female Normal Donor Norvasc,Ditropan Control77 Male Normal Donor, Lipitor 20 mg, Tricor 145 mg, Metoprolol Cataract,Progressive 50 mg, Omeprazole 20 mg, Aspirin 80 mg Hearing Loss,Hypertension (HTN), Hypercholesterolemia Control 56 Male Normal DonorNexium 60 mg, Zocor 40 mg Control 64 Male Normal Donor Protonix 40 mg,Asiprin 325 mg Control 80 Male Normal Donor Vitamin D, Lipitor, Aspirin81 mg Control 73 Female Normal Donor, Allegra 180 mg Allergic RhinitisControl 77 Female Normal Donor None Control 83 Male Normal Donor NoneControl 68 Female Normal Donor Losartan 50 mg, Lipitor 20 mg Control 66Female Normal Donor, Lisinopril 10 mg Hypertension (HTN) Control 78Female Normal Donor Lipitor 10 mg, Toprol 50 mg, Ambien 10 mg Control 86Female Normal Donor, Amlodipine 2.5 mg, Vitamin B Hypertension (HTN)

TABLE 16 CRC and Controls Class Age Gender Diagnosis Diseased 74 FemaleColorectal Cancer Diseased 41 Male Colorectal Cancer Diseased 57 MaleColorectal Cancer, Anemia (Iron Deficiency), Type 2 Diabetes Diseased 78Male Colorectal Cancer, Chronic Lymphocytic Leukemia (CLL), Hypertension(HTN), Type 2 Diabetes, Arthritis Diseased 60 Female Colorectal Cancer,CKD, Iron Deficiency, RLS, Carcinoma of Colon, Carcinoma of Right Ovary,Anxiety Diseased 37 Male Colorectal Cancer, Erectile Dysfunction,Thrombocytopenia Diseased 68 Female Colorectal Cancer, Major DepressiveDisorder (MDD), Type 2 Diabetes, Hernia with Obstruction, VentralHernia, Dysphonia, Hypercholesterolemia, Hyperlipidemia, Hypertension(HTN), Migraine, Obesity, Diabetic Polyneuropathy, Reflux Esophagitis,Edema, Asthma, Chronic Obstructive Pulmonary Disease (COPD), E CoilBacteremia, Subdural Hematoma, Hepatic Abscess Diseased 60 FemaleColorectal Cancer, Rectal Cancer, Type 1 Diabetes, Hypertension (HTN),Hypothyroidism Control 75 Female Normal Donor Control 42 Male NormalDonor Control 56 Male Normal Donor Control 78 Male Normal Donor Control58 Female Normal Donor Control 36 Male Normal Donor Control 68 FemaleNormal Donor Control 59 Female Normal Donor

Example 13 Characterization of Physicochemical Properties of ParticleTypes

This example describes characterization of particle physicochemicalproperties by various techniques. Dynamic light scattering (DLS) andzeta potential were performed on a Zetasizer Nano ZS(MalvermInstruments, Worcestershire, UK). Particles were suspended at 10mg/mL in water with about 10 min of bath sonication prior to testing.Samples were then diluted to approximately 0.02 wt % of or both DLS andzeta potential measurements in respective buffers. DLS was performed inwater at about 25° C. in disposable polystyrene semi-micro cuvettes(VWR, Randor, PA, USA) with a about 1 min temperature equilibration timeand consisted of the average from 3 runs of about 1 min, with a 633 nmlaser in 173 backscatter mode. DLS results were analyzed using thecumulants method. Zeta potential was measured in 5% pH 7.4 PBS (Gibco,PN 10010-023, USA) in disposable folded capillary cells (MalvernInstruments, PN DTS1070) at about 25° C. with an about 1 minequilibration time. 3 measurements were performed with automaticmeasurement duration with a minimum of 10 runs and a maximum of 100runs, and a 1 min hold between measurements. The Smoluchowski model wasused to determine the zeta potential from the electrophoretic mobility.

Scanning electron microscopy (SEM) was performed by using a FEI Helios600 Dual-Beam FIB-SEM. Aqueous dispersions of particles were prepared toa concentration of about 10 mg/mL from weighted particle powdersre-dispersed in DI water by about 10 min sonication. Then, the sampleswere 4× diluted by methanol (from Fisher) to make a dispersion inwater/methanol that was directly used for electron microscopy. The SEMsubstrates were prepared by drop-casting about 6 μL of particle sampleson the Si wafer from Ted Pella, and then the droplet was completelydried in a vacuum desiccator for about 24 hours prior to measurements.

A Titan 80-300 transmission electron microscope (TEM) with anaccelerating voltage of 300 kV was used for both low- andhigh-resolution TEM measurements. The TEM grids were prepared bydrop-casting about 2 μL of the particle dispersions in water-methanolmixture (25-75 v/v %) with a final concentration of about 0.25 mg/mL anddried in a vacuum desiccator for about 24 hours prior to the TEManalysis. All measurements were performed on the lacey holey TEM gridsfrom Ted Pella.

X-Ray Photoelectron Spectroscopy (XPS) was performed by using a PHIVersaProbe and a ThermoScientific ESCALAB 250e III. XPS analysis wasperformed on the particle fine powders kept sealed and stored underdesiccation prior to the measurements. Materials were mounted on acarbon tape to achieve a uniform surface for analysis. A monochromaticAl K-alpha X-ray source (50 W and 15 kV) was used over a 200 μm² scanarea with a pass energy of 140 eV, and all binding energies werereferenced to the C—C peak at 284.8 eV. Both survey scans andhigh-resolution scans were performed to assess in detail elements ofinterest. The atomic concentration of each element was determined fromintegrated intensity of elemental photoemission features corrected byrelative atomic sensitivity factors by averaging the results from twodifferent locations on the sample. In some cases, four or more locationswere averaged to assess uniformity.

Example 14 Protein Corona Preparation and Proteomic Analysis

This example describes protein corona preparation and proteomicanalysis. Plasma and serum samples were diluted 1:5 in a dilution buffercomposed of TE buffer (10 mM Tris, 1 mM disodium EDTA, 150 mM KCl) with0.05% CHAPS. Particle powder was reconstituted by sonicating for about10 min in DI water followed by vortexing for about 2-3 sec. To make aprotein corona, about 100 μL of particle suspension (SP-003, 5 mg/ml;SP-007, 2.5 mg/ml; SP-011, 10 mg/ml) was mixed with about 100 μL ofdiluted biological samples in microtiter plates. The plates were sealedand incubated at 37° C. for about 1 hour with shaking at 300 rpm. Afterincubation, the plate was placed on top of magnetic collection for about5 mins to pellet down the nanoparticles. Unbound proteins in supernatantwere pipetted out. The protein corona was further washed with about 200μL of dilution buffer for three times with magnetic separation. For the10 particle type particle panel screen, the five additional assayconditions that were evaluated were identical to the description abovewith one of the following exceptions. First, a low concentration ofparticles was evaluated that was 50% the concentration of the originalparticle concentration (ranging from 2.5-15 mg/ml for each particle,depending on expected peptide yield). For the second and third assayvariations, both low and high particle concentrations were run using anundiluted, neat plasma rather than diluting the plasma in buffer. Forthe fourth and fifth assay variations, both low and high particleconcentrations were run using a pH 5 citrate buffer for both dilutionand rinse.

To digest the proteins bound onto nanoparticles, a trypsin digestion kit(iST 96X, PreOmics, Germany) was used according to protocols provided.Briefly, about 50 μL of Lyse buffer was added to each well and heated atabout 95° C. for about 10 min with agitation. After cooling down theplates to room temperature, trypsin digest buffer was added and theplate was incubated at about 37° C. for about 3 hours with shaking. Thedigestion process was stopped with a stop buffer. The supernatant wasseparated from the nanoparticles by a magnetic collector and furthercleaned up by a peptide cleanup cartridge included in the kit. Thepeptide was eluted with about 75 μL of elution buffer twice andcombined. Peptide concentration was measured by a quantitativecolorimetric peptide assay kit from Thermo Fisher Scientific (Waltham,Mass.).

Next, the peptide eluates were lyophilized and reconstituted in 0.1%TFA. A 2 μg aliquot from each sample was analyzed by nano LC-MS/MS witha Waters NanoAcquity HPLC system interfaced to an Orbitrap Fusion LumosTribrid Mass Spectrometer from Thermo Fisher Scientific. Peptides wereloaded on a trapping column and eluted over a 75 μm analytical column at350 nL/min; both columns were packed with Luna C18 resin from Phenomenex(Torrance, Calif.). The mass spectrometer was operated in adata-dependent mode, with MS and MS/MS performed in the Orbitrap at60,000 FWHM resolution and 15,000 FWHM resolution, respectively. Theinstrument was run with a 3 sec cycle for MS and MS/MS.

Example 15 Mass Spectrometry Data Analysis

This example describes mass spectrometry data analysis methods. Theacquired MS data files were processed using the OpenMS suite of tools.These tools include modules and pipeline scripts for the conversion ofvendor instrument raw files to mzML files, for MS1 featureidentification and intensity extraction, for MS dataset run-timealignment and feature-group clustering, and for MS2 spectrum databasematching with the X! Tandem search engine. During spectrum-databasesearching the precursor ion and fragment ion matching tolerances wereset to 10 and 30 ppm, respectively. Default settings for fixed,Carbamidomethyl (C), and variable, Acetyl (N-term) and Oxidation (M),modifications were enabled. The UniProtKB/Swiss-Prot protein sequencedatabase (accession date Jan. 27, 2019) was used for searches andpeptide spectral matches (PSMs) were scored using a standardreverse-sequence decoy database strategy at 1% FDR. Using the PSMs,protein lists for each particle type replicate were compiled using asingle PSM as sufficient evidence to add a protein to a given particletype replicate's enumerated protein list. In addition, a PSM thatmatched more than one protein added all of the possible proteins to thegiven particle type replicate's enumerated protein list. Although thisthreshold for protein enumeration is permissive, and possibly includesfalse-positives (higher sensitivity, lower specificity), the morestringent test of requiring 2 or more peptides (including at least oneunique peptide) suffers from the opposite problem of havingfalse-negatives (lower sensitivity, higher specificity). Forquantitative analysis of known peptides, a custom R script was used toassign MS2 PSMs to MS1 feature groups based on positional overlap with 1da and 30 sec tolerances for mz and retention time, respectively. In theevent that more than one PSM initially mapped to an MS1 feature withinthe tolerances previously specified, the PSM which was closest to theMS1 feature (within MS datasets) or to the center of the MS1 featurecluster (between MS datasets) was used. It should be noted that not allMS2s have been assigned to MS1 feature group clusters, and not all MS1feature group clusters have an assigned MS2; work continues in this areato improve mapping and subsequent peptide feature identification.

Example 16 Identification of Protein Groups

This example describes methods for identification of protein groups bymass spectrometry. For protein group-level analysis, the MS data at theprotein group level was performed as follows. MS raw files wereprocessed with MaxQuant (v. 1.6.7(49) and Andromeda(50), searching MS/MSspectra against the UniProtKB human FASTA database (UP000005640, 74,349forward entries; version from August 2019) employing standard settings.Enzyme digestion specificity was set to trypsin allowing cleavageN-terminal to proline and up to 2 miscleavages. Minimum peptide lengthwas set to 7 amino acids and maximum peptide mass was set to 4,600 Da.Methionine oxidation and protein N-terminus acetylation wereconfigurated as a variable modification, carbamidomethylation ofcysteines was set as fixed modification. MaxQuant improves precursor ionmass accuracy by time-dependent recalibration algorithms and definesindividual mass tolerances for each peptide. Initial maximum precursormass tolerances allowed were 20 ppm during the first search and 4.5 ppmin the main search. The MS/MS mass tolerance was set to 20 ppm. Foranalysis, a false discovery rate (FDR) cutoff of 1% was applied at thepeptide and protein level (in the proteinGroups.txt table, all proteingroups are reported with their corresponding q-value). “Match betweenruns,” was disabled. Number of identifications where counted based onprotein intensities (counting only proteins with q-value lower than 1%)requiring at least one razor peptide. MaxLFQ normalized proteinintensities (requiring at least 1 peptide ratio count) are reported inthe raw output and were used only for the CV precision analysis.Peptides that could be distinguished were sorted into their own proteingroups and proteins that could not be discriminated based on uniquepeptides were assembled in protein groups. Furthermore, proteins werefiltered for a list of common contaminants included in MaxQuant.Proteins identified only by site modification were strictly excludedfrom analysis.

Example 17 Spike Recovery

This example describes methods for spike recovery experiments ofC-reactive protein (CRP). Baseline concentration of CRP in a pooledhealthy plasma sample was measured with the ELISA kit as described above(Materials) according to the manufacturer-suggested protocols. A stocksolution and appropriate dilutions of CRP were prepared and spiked intothe identical pooled plasma samples to make final concentrations thatwere 2×, 5×, 10×, and 100× of baseline, endogenous concentrations forCRP. The volume of additions to the pooled plasma was 10% of the totalsample volume. A spike control was made by adding same volume of bufferto the pooled plasma sample. Concentrations of spiked samples weremeasured again by ELISA to confirm the CRP levels in each spiking level.The samples were used to evaluate particle corona measurement accuracyas described in the Results above.

Example 18 Proteomic Analysis of NSCLC Samples and Healthy Controls

This example describes proteomic analysis of NSCLC samples and healthcontrols. Serum samples from 56 subjects, 28 with Stage IV NSCLC and 28age- and gender-matched controls were purchased commercially andevaluated with SP-007 nanoparticle corona formation (see above forsample acquisition and corona formation and processing). MS spectraldata for each corona were collected as described and the raw data wereprocessed as described above (MS data analysis). 19,214 groups offeatures were identified and extracted across the 56 subject sampleswith group sizes ranging from one (singleton features in just onesample, n=6,249 or 0.29% of the data) to 56 (features present in allsamples, n=450 or 12% of the data). The clustering algorithm calculatesa ‘group_quality’ metric which is related to the spatial uniformity ofgrouping of features with groups between datasets. The bottom quartileof groups, partitioned by group size, was then removed fromconsideration due to the skewed nature of the distribution oflow-quality scores leaving 15,967 groups. As an additional filter priorto analysis, only those groups with features present in at least 50% ofat least one of the classes, diseased or control, were carried forwardleaving a set of 2,507 feature groups for analysis.

Peptide and protein identities were assigned to the feature groups asfollows. MS2 PSMs and MS1 feature groups were assigned together asdescribed above (MS data analysis). 25% of the 19,249 original featuregroups were associated with a peptide sequence using this approach. Allfeature groups, with or without assigned peptide sequence, were carriedthrough the univariate statistical comparison between the groups.

Example 19 Statistical Analysis

This example describes statistical analysis of the data disclosedherein. Statistical analysis and visualization were performed using R(v3.5.2) with appropriate packages (R: A language and environment forstatistical computing. R Foundation for Statistical Computing, Vienna,Austria. URL https://www.R-project.org/).

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention. It is intended thatthe following claims define the scope of the invention and that methodsand structures within the scope of these claims and their equivalents becovered thereby.

What is claimed is:
 1. A composition comprising three or more distinctmagnetic particle types that differ by two or more physicochemicalproperties, wherein a subset of the three or more distinct magneticparticle types share a physicochemical property of the two or morephysicochemical properties and wherein such particle types of the subsetbind different proteins.
 2. The composition of claim 1, wherein thethree or more distinct magnetic particle types adsorb proteins from asample over a dynamic range of at least
 7. 3. The composition of claim1, wherein the three or more distinct magnetic particle types arecapable of adsorbing from 1,000 to 10,000 protein groups from a sample.4. The composition of claim 3, wherein the protein groups comprise apeptide sequence having a minimum length of 7 amino acid residues. 5.The composition of claim 1, wherein the composition comprises at least 4distinct magnetic particle types.
 6. The composition of claim 5, whereinthe composition comprises at least 10 distinct magnetic particle types.7. The composition of claim 1, wherein the composition comprises a firstdistinct particle type and a second distinct particle type, wherein thefirst distinct particle type and the second distinct particle type shareat least two physicochemical properties and differ by at least twophysicochemical properties, such that the first distinct particle typeand the second distinct particle type are different.
 8. The compositionof claim 1, wherein the physicochemical property comprises size, charge,core material, shell material, porosity, or surface hydrophobicity. 9.The composition of claim 1, wherein the composition comprises a firstdistinct particle type and a second distinct particle type, wherein thefirst distinct particle type and the second distinct particle typecomprise a carboxylate material, wherein the first distinct particle isa microparticle, and wherein the second distinct particle type is ananoparticle.
 10. The composition of claim 1, wherein the three or moredistinct magnetic particle types comprise a nanoparticle or amicroparticle.
 11. The composition of claim 1, wherein at least onedistinct particle type of the three or more distinct magnetic particletypes is a superparamagnetic iron oxide particle.
 12. The composition ofclaim 1, wherein at least one distinct particle type of the three ormore distinct magnetic particle types comprise an iron oxide material.13. The composition of claim 1, wherein the three or more distinctmagnetic particle types comprise one or more particle types of TABLE 10.14. The composition of claim 1, wherein at least one particle type ofthe three or more distinct magnetic particle types comprises a polymercoating.
 15. The composition of claim 14, wherein the polymer costingcomprises a carboxylated polymer, an aminated polymer, a zwitterionicpolymer, or any combination thereof.
 16. The composition of claim 1,wherein at least one particle type of the three or more distinctmagnetic particle types comprises an iron oxide core with a silica shellcoating, a poly(N-(3-(dimethylamino)propyl) methacrylamide) (PDMAPMA)coating, or a poly(oligo(ethylene glycol) methyl ether methacrylate)(POEGMA) coating.
 17. The composition of claim 1, wherein at least oneparticle type of the three or more distinct magnetic particle typescomprises a negative surface charge, a positive surface charge, or aneutral surface charge.